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Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier. [ABSTRACT FROM PUBLISHER]

Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results. [ABSTRACT FROM PUBLISHER]

This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact models by selecting the adequate variables and adapting the number of fuzzy labels for each problem, improving the interpretability of the model. The experimental analysis is carried out over a wide range of highly imbalanced data-sets and uses the statistical tests suggested in the specialized literature. [ABSTRACT FROM AUTHOR]

Abstract: Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics. The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios. The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification. [Copyright &y& Elsevier]

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called 'variable mesh optimization' (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In supervised classification, we often encounter many real world problems in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. One of the most used techniques to deal with this problem consists of preprocessing the data previously to the learning process. This paper proposes a method belonging to the family of the nested generalized exemplar that accomplishes learning by storing objects in Euclidean n-space. Classification of new data is performed by computing their distance to the nearest generalized exemplar. The method is optimized by the selection of the most suitable generalized exemplars based on evolutionary algorithms. An experimental analysis is carried out over a wide range of highly imbalanced data sets and uses the statistical tests suggested in the specialized literature. The results obtained show that our evolutionary proposal outperforms other classic and recent models in accuracy and requires to store a lower number of generalized examples. [Copyright &y& Elsevier]

Abstract: The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature. [Copyright &y& Elsevier]

The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve classification and pattern recognition tasks. Despite its high classification accuracy, this rule suffers from several shortcomings in time response, noise sensitivity, and high storage requirements. These weaknesses have been tackled by many different approaches, including a good and well-known solution that we can find in the literature, which consists of the reduction of the data used for the classification rule (training data). Prototype reduction techniques can be divided into two different approaches, which are known as prototype selection and prototype generation (PG) or abstraction. The former process consists of choosing a subset of the original training data, whereas PG builds new artificial prototypes to increase the accuracy of the NN classification. In this paper, we provide a survey of PG methods specifically designed for the NN rule. From a theoretical point of view, we propose a taxonomy based on the main characteristics presented in them. Furthermore, from an empirical point of view, we conduct a wide experimental study that involves small and large datasets to measure their performance in terms of accuracy and reduction capabilities. The results are contrasted through nonparametrical statistical tests. Several remarks are made to understand which PG models are appropriate for application to different datasets. [ABSTRACT FROM PUBLISHER]

Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration reached by this one. Using this technique, it was presented a memetic algorithm, MA-CMA-Chains, using the CMA-ES algorithm as its local search component. This proposal obtained very good results for continuous optimisation problems, in particular with medium-size (with up to dimension 50). Unfortunately, CMA-ES scalability is restricted by several costly operations, thus MA-CMA-Chains could not be successfully applied to large scale problems. In this article we study the scalability of memetic algorithms based on local search chains, creating memetic algorithms with different local search methods and comparing them, considering both the error values and the processing cost. We also propose a variation of Solis Wets method, that we call Subgrouping Solis Wets algorithm. This local search method explores, at each step of the algorithm, only a random subset of the variables. This subset changes after a certain number of evaluations. Finally, we propose a new memetic algorithm based on local search chains for high dimensionality, MA-SSW-Chains, using the Subgrouping Solis Wets' algorithm as its local search method. This algorithm is compared with MA-CMA-Chains and different reference algorithms, and it is shown that the proposal is fairly scalable and it is statistically very competitive for high-dimensional problems. [ABSTRACT FROM AUTHOR]

In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a preprocessing step. In this paper we analyze the usefulness of the data complexity measures in order to evaluate the behavior of undersampling and oversampling methods. Two classical learning methods, C4.5 and PART, are considered over a wide range of imbalanced data sets built from real data. Specifically, oversampling techniques and an evolutionary undersampling one have been selected for the study. We extract behavior patterns from the results in the data complexity space defined by the measures, coding them as intervals. Then, we derive rules from the intervals that describe both good or bad behaviors of C4.5 and PART for the different preprocessing approaches, thus obtaining a complete characterization of the data sets and the differences between the oversampling and undersampling results. [ABSTRACT FROM AUTHOR]

Abstract: Classification problems involving multiple classes can be addressed in different ways. One of the most popular techniques consists in dividing the original data set into two-class subsets, learning a different binary model for each new subset. These techniques are known as binarization strategies. In this work, we are interested in ensemble methods by binarization techniques; in particular, we focus on the well-known one-vs-one and one-vs-all decomposition strategies, paying special attention to the final step of the ensembles, the combination of the outputs of the binary classifiers. Our aim is to develop an empirical analysis of different aggregations to combine these outputs. To do so, we develop a double study: first, we use different base classifiers in order to observe the suitability and potential of each combination within each classifier. Then, we compare the performance of these ensemble techniques with the classifiers'' themselves. Hence, we also analyse the improvement with respect to the classifiers that handle multiple classes inherently. We carry out the experimental study with several well-known algorithms of the literature such as Support Vector Machines, Decision Trees, Instance Based Learning or Rule Based Systems. We will show, supported by several statistical analyses, the goodness of the binarization techniques with respect to the base classifiers and finally we will point out the most robust techniques within this framework. [Copyright &y& Elsevier]

The "ISI rankings of the spanish universities by scientific fields" report is described, and some of the results are presented. One of the essential characteristics of this new ranking, based on Thomson-Reuters products, is its organization into 12 fields of knowledge. The IFQ2A-Index indicator was used to sort the universities, synthesizing information from 6 bibliometric indicators. The results section presents some total indicators for the Spanish university system, showing a steady growth of scientific production but almost no change in the visibility of the journals. Furthermore, a detailed analysis of the results shows the dominance of Catalan universities in the 12 fields of knowledge analyzed during the period 2005-2009. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with multi-class classification for linguistic fuzzy rule based classification systems. The idea is to decompose the original data-set into binary classification problems using the pairwise learning approach (confronting all pair of classes), and to obtain an independent fuzzy system for each one of them. Along the inference process, each fuzzy rule based classification system generates an association degree for both of its corresponding classes and these values are encoded into a fuzzy preference relation. Our analysis is focused on the final step that returns the predicted class-label. Specifically, we propose to manage the fuzzy preference relation using a non-dominance criterion on the different alternatives, contrasting the behaviour of this model with both the classical weighted voting scheme and a decision rule that combines the fuzzy relations of preference, conflict and ignorance by means of a voting strategy. Our experimental study is carried out using two different linguistic fuzzy rule learning methods for which we show that the non-dominance criterion is a good alternative in comparison with the previously mentioned aggregation mechanisms. This empirical analysis is supported through the corresponding statistical analysis using non-parametrical tests. [ABSTRACT FROM AUTHOR]

Abstract: In this work morphological, structural and photoelectrochemical properties of n-type α-Fe2O3 (hematite) thin films synthetized by means of two different electrochemical procedures: potential cycling electrodeposition (PC) and potential pulsed electrodeposition (PP) have been studied. The X-ray diffraction measurements showed that the films obtained after a thermal treatment at 520°C present a nanocrystalline character. Scanning electron microscopy allowed finding that hematite films obtained by PP technique exhibit nanostructured morphology. The electrochemical and capacitance (Mott–Schottky and parallel capacitance) measurements showed that when in the PC and PP procedures the anodic limit Eλ,A is being made more anodic, a decrease of the majority carriers concentration (ND) and the surface states number has been observed. The photovoltammetry measurements indicated that the hematite films formed with the PP technique present a photocurrent one order of magnitude higher than the ones exhibited by the iron oxide films formed by PC. For instance, PP hematite films exhibit photovoltaic conversion efficiencies of 0.96% which are 2.5 times higher than the corresponding to the PC ones (0.38%). The maximum incident photon-to-current efficiency measured at λ=370 and 600nm was observed for hematite films grown by the PP procedure. By means of the photocurrent transient technique a decrease in the recombination process for those samples synthesized by PP was observed. The results obtained are discussed considering the influence of the anodic limit of the potential employed during the preparation of the iron oxyhydroxide (β-FeOOH) precursor film, all of this related to a decrease of the oxygen defects in this material and to a decrease of Fe(II) amount that is formed during the electrodeposition process. [ABSTRACT FROM AUTHOR]

Purpose: Given the availability of laparoscopy and the rising detection of incidentalomas, indications for adrenalectomy may be changing. The Endocrine Surgery Section of the Spanish Association of Surgeons designed a survey to assess its indications, techniques, and results in Spanish Surgical Departments. Methods: Collected data included hospital and department type, yearly hospital volume of procedures; location studies and preoperative preparation performed, indications, surgical approach and instruments used, and results in terms of morbidity and overall hospital stay. The analysis included a comparison between results of high- or low-volume centers and surgeons, using the Student's t test for quantitative and chi-square test for qualitative variables. Level of significance was set at 0.05. Results: Nineteen centers returned the questionnaire, including 155 adrenalectomies performed in 2008. Most frequent indications were pheochromocytoma (23.2%), aldosteronoma (16.7%), incidentaloma (12.2%), metastasis (10.3%), Cushing adenoma (9.6%), and carcinoma (3.8%). Laparoscopy was performed in 83.9% of cases (9.4% required conversion to laparotomy). Four patients required urgent reoperation. Average hospital stay: 4.6 days (3.3 days after laparoscopy, 7 days after laparotomy). High-volume centers had a greater proportion of laparoscopically treated cases ( p = 0.008), more malignant lesions treated ( p = 0.03), a shorter overall stay ( p < 0.0001), and a shorter stay after laparotomic adrenalectomy ( p = 0.01). High-volume surgeons had similar results, and less in-hospital morbidity ( p = 0.02). Conclusions: In Spain, adrenalectomy is performed in hospitals of varying complexity. Laparoscopic approach is the rule, with good results in terms of morbidty and stay. High-volume centers and surgeons had best results in terms of use of minimally invasive surgery and hospital stay. [ABSTRACT FROM AUTHOR]

Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition; however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented. [ABSTRACT FROM AUTHOR]

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that can effectively use intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the newmemetic algorithm model, an instance is implemented with CMA-ES as an intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems. [ABSTRACT FROM AUTHOR]

DataMining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed. [ABSTRACT FROM AUTHOR]

Evolutionary prototype selection has shown its effectiveness in the past in the prototype selection domain. It improves in most of the cases the results offered by classical prototype selection algorithms but its computational cost is expensive. In this paper, we analyze the behavior of the evolutionary prototype selection strategy, considering a complexity measure for classification problems based on overlapping. In addition, we have analyzed different k values for the nearest neighbour classifier in this domain of study to see its influence on the results of PS methods. The objective consists of predicting when the evolutionary prototype selection is effective for a particular problem, based on this overlapping measure. [ABSTRACT FROM AUTHOR]

The article focuses on the findings of a study concerning the significance of patient satisfaction for ambulatory anesthesia. The study conducts a systematic review to evaluate questionnaires to measure patient satisfaction with ambulatory anesthesia. Researchers scanned 131 articles yielded by the search strategy and 11 articles were included in the study. It mentions that only a few studies have developed questionnaires with rigorous psychometric methods to measure patient satisfaction with anesthesia care. Moreover, it determines that there is still no valid questionnaire for measuring patient satisfaction in ambulatory anesthesia.

Abstract: Different studies have proposed methods for mining fuzzy association rules from quantitative data, where the membership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extracting both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic term membership functions. Experimental results show the effectiveness of the framework. [Copyright &y& Elsevier]

Abstract: In the field of classification problems, we often encounter classes with a very different percentage of patterns between them, classes with a high pattern percentage and classes with a low pattern percentage. These problems receive the name of “classification problems with imbalanced data-sets”. In this paper we study the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets, focusing on the synergy with the preprocessing mechanisms of instances and the configuration of fuzzy rule based classification systems. We will analyse the necessity of applying a preprocessing step to deal with the problem of imbalanced data-sets. Regarding the components of the fuzzy rule base classification system, we are interested in the granularity of the fuzzy partitions, the use of distinct conjunction operators, the application of some approaches to compute the rule weights and the use of different fuzzy reasoning methods. [Copyright &y& Elsevier]

Dealing with incomplete information is an important problem in decision making. In this paper, we present a short discussion on this topic and a new estimation method of missing values in an incomplete fuzzy preference relation which is based on the modelling of consistency of preferences via a representable uninorm. [ABSTRACT FROM AUTHOR]

The Authors regret that, in the published version, Figure 2 was substituted by Figure 1. [ABSTRACT FROM AUTHOR]

Objective To determine the therapeutic efficacy and safety of a nitric oxide (NO) isosorbide dinitrate donor to induce cervical ripening of women with missed abortions before surgical evacuation of the uterus. Design A prospective, randomised, double-blind controlled trial. Setting Tertiary referral maternity teaching hospital. Population Sixty women with missed abortions and no cervical dilation. Methods Women requesting surgical evacuation of the uterus were randomly selected to receive endocervical 80 mg/1.5 mL isosorbide dinitrate gel solution ( n= 30) or 400 μg/1.5 mL misoprostol gel solution ( n= 30) every 3 hours to a maximum of four doses or until reaching cervical ripening. Vital signs and symptoms were recorded at baseline and then every 3 hours until finishing therapy. Adverse events, such as headache, abdominal pain, pelvic pain, backache, nausea and vomiting, were evaluated. Main outcome measures Probability of reaching cervical ripening >8 mm Hegar dilator; evaluated at 3, 6, 9 and 12 hours after application of isosorbide dinitrate or misoprostol. Results The probabilities of induction of cervical ripening by isosorbide dinitrate and misoprostol after four repeated doses at 3-hour intervals were significantly different ( P < 0.001). Efficacy of therapy after 12 hours was 97% for the isosorbide dinitrate group and 70% for the misoprostol group. Systolic and diastolic blood pressures were lower after administration of isosorbide dinitrate than prostaglandin analogues. The difference in the mean systolic and diastolic blood pressure between treatment groups was greatest at 3 hours, with a difference of 7.7 mmHg ( P < 0.001) and 5.9 mmHg ( P < 0.003), respectively. The most frequent side effect associated with isosorbide dinitrate administration was headache, which occurred in 18 out of 30 patients, compared with only 5 out of 30 women in the misoprostol group [relative risk (RR) 2.41, 95% confidence interval (CI) 1.45–4.03, P < 0.001). Women treated with misoprostol reported mainly pelvic pain (RR 3.24, 95% CI 1.99–5.27, P < 0.001). Conclusions Intracervical administration of 80 mg isosorbide dinitrate in women with missed abortions appears to be effective for cervical ripening prior to surgical evacuation of the uterus. Differences in the incidence of non-serious adverse events are not likely to be clinically significant. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose the use of weighted linguistic fuzzy rules in combination with a rule selection process to develop accurate fuzzy logic controllers dedicated to the intelligent control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. To do so, a genetic optimization process considering an efficient approach to perform rule weight derivation and rule selection is developed. This allows the tuning of the system to be developed at the rule level. The proposed technique was tested considering a physical modelization of a real test site. [Copyright &y& Elsevier]

The gonad morphology of the sea cucumber Isostichopus badionotus, collected during the months of May to November 1996 in Morrocoy Bay on the northwestern Venezuelan coast, was analyzed. The gonadal cycle was characterized by five stages of development: post-spawning, recovery, growth, advanced growth and maturity. Maturation did not proceed at the same pace in all gonad tubules of any one animal. Due to this asynchronous gonad development an Individual Weighted Maturity Index (IWMI) was devised to determine reproductive state. It was calculated from the proportion of the different tubule stages observed in each specimen. The maximum value attainable is 5 if all tubules are in the mature stage. Towards the months of July and August, most, but not all, of the ovarian and testicular tubules had reached maturity as indicated by IWMI values of 4.32 and 4, respectively. IWMI represents a quantitative estimation of gonad maturation in holothurians exhibiting asynchronous development as it revealed the maturation pattern underlying gonadal chronological development. [Copyright &y& Elsevier]

This paper presents an evolutionary learning process for linguistic modeling with weighted double-consequent fuzzy rules. These kinds of fuzzy rules are used to improve the linguistic modeling, with the aim of introducing a trade-off between interpretability and precision. The use of weighted double-consequent fuzzy rules makes more complex the modeling and learning process, increasing the solution search space. Therefore, the cooperative coevolution, an advanced evolutionary technique proposed to solve decomposable complex problems, is considered to learn these kinds of rules. The proposal has been tested with different problems achieving good results. [ABSTRACT FROM AUTHOR]

The use of Mamdani-type fuzzy rule-based systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired. This fact relates to the restriction imposed when using linguistic variables, which forces the membership functions considered in each fuzzy linguistic rule to belong to a common set of them, i.e., to use a global grid. To solve this problem, in the last few years a new variant has been proposed working directly with fuzzy variables in the fuzzy rules instead of linguistic terms, thus ignoring the said restriction. Therefore, these systems, which are totally equivalent to fuzzy graphs (defined by Zadeh as granular representations of functional dependencies and relations), do not consider a global grid and could be named non-grid-oriented (NGO) FRBSs. Of course, the main objective of these models is the accuracy of the system instead its interpretability. Until now, NGO FRBSs have been little considered and developed in the literature. However, and due to their good accuracy, their use is increasing thus making necessary a wide analysis on the features and associated learning methods in the NGO domain. This contribution aims at analyzing the structure and framework of NGO FRBSs, as well as making a taxonomy of learning methods considering the constrains imposed on the fuzzy sets in the generation process. Some automatic learning techniques and methods proposed in the literature to build these fuzzy graphs will be also reviewed and analyzed when solving several applications of different nature. [ABSTRACT FROM AUTHOR]

In Deep Learning, training a model properly with a high quantity and quality of data is crucial in order to achieve a good performance. In some tasks, however, the necessary data is not available at a particular moment and only becomes available over time. In which case, incremental learning is used to train the model correctly. An open problem remains, however, in the form of the stability–plasticity dilemma: how to incrementally train a model that is able to respond well to new data (plasticity) while also retaining previous knowledge (stability). In this paper, an incremental learning model inspired in Rehearsal (recall of past memories based on a subset of data) named CRIF is proposed, and two instances for the framework are employed — one using a random-based selection of representative samples (Naive Incremental Learning, NIL), the other using Crowding Distance and Best vs. Second Best metrics in conjunction for this task (RILBC). The experiments were performed on five datasets — MNIST, Fashion-MNIST, CIFAR-10, Caltech 101, and Tiny ImageNet, in two different incremental scenarios: a strictly class-incremental scenario, and a pseudo class-incremental scenario with unbalanced data. In Caltech 101, Transfer Learning was used, and in this scenario as well as in the other three datasets, the proposed method, NIL, achieved better results in most of the quality metrics than comparison algorithms such as RMSProp Inc (base line) and iCaRL (state-of-the-art proposal) and outperformed the other proposed method, RILBC. NIL also requires less time to achieve these results. • An incremental learning model inspired in Rehearsal (recall of past memories based on a subset of data) is proposed. • Experiments were performed over MNIST, Fashion-MNIST, CIFAR-10 and Caltech 101 in two different scenarios. • Several metrics were used to compare learning quality results when each new megabatch of data is used. • Friedman's non-parametric statistical test and Holm post-hoc test were used for supporting the analysis of the results. • Random-based selection of representative samples obtains the best results. [ABSTRACT FROM AUTHOR]

Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases' distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases' specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results. • In a manual testing procedure, all testing artifacts are written in a natural text, employing natural language processing techniques might provide highly useful information for test optimization purposes. • The ratio of dependent and independent test cases might suffer from an imbalanced distribution due to the testing level and complexity of the system under test. • Doc2Vec proves to be a good tool when transforming the manual test cases into feature vectors. • IFROWANN performs well when splitting dependent and independent test cases as an imbalance learning algorithm. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses an article on genetic fuzzy data mining, an article on Mamdani fuzzy systems, and the application of subgroup discovery methods for analysis of patients and the arrival time in Psychiatric Emergency Department.

No abstract received. [ABSTRACT FROM AUTHOR]

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. 'number of unique Twitter users', improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature's early impact. [ABSTRACT FROM AUTHOR]

This study addresses the synthesis and characterization of iron(III) hexacyanoferrate(II) (Prussian blue, PB) deposited on a semiconductor hematite electrode (α-Fe 2 O 3). The formation of the Prussian blue/hematite interface was carried out by cyclic voltammetry. The modified electrode was morphologically characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM), structurally by x-ray diffraction (XRD), and photoelectrochemically by cyclic voltammetry under polychromatic illumination. The structure of the electrode system shows a cauliflower type morphology, with the typical PB structure. The electrochemical characterization indicates an increase of the currents of the low- and high-spin iron systems of PB when the system is illuminated, indicating that photogenerated charge carriers are involved. • The photogenerated charge carriers in α-Fe 2 O 3 can be transferred to Prussian blue increasing the charge of the redox process. • The electrochemical deposition of Prussian blue (PB) on α-Fe 2 O 3 shows a morphology cauliflower type like. • The stability of PB redox signals on α-Fe 2 O 3 depend on the extension of the potential window applied on electrode system. [ABSTRACT FROM AUTHOR]

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. [ABSTRACT FROM AUTHOR]

This paper focuses on multi-attribute intuitionistic fuzzy large-scale decision making (LSDM) scenarios. The alternatives are described by attributes in the LSDM model. The decision failure may be caused by unqualified alternative being the final decision. To avoid this, we propose a Defective Alternative Detection-based multi-attribute intuitionistic fuzzy LSDM (DAD-LSDM) model. The model consists of two stages: the Defective Alternatives Detection (DAD) stage and the corresponding Intuitionistic Fuzzy Consensus Reaching Process (IF-CRP) stage. In the DAD stage, it is easy to recognize the defective alternatives by calculating the attributes' scores and to accordingly improve them with the attributes against the corresponding alternatives. In the IF-CRP stage, by utilizing an intuitionistic fuzzy clustering method and similarity calculation, we detect and manage the potential non-cooperative decision makers to increase the consensus degree of the LSDM event. By implementing the DAD stage before the IF-CRP stage, we can avoid those excessively defective alternatives to be chosen and can also improve the quality of slightly defective alternatives. It not only decreases the risk of decision failure and improves the feasibility of the provided alternatives, but also guarantees the validity and scientificity of the following IF-CRP stage. With a numerical example, we show the DAD-LSDM model can well detect and classify the defective alternatives as well as improve the slightly defective alternatives. The decision makers finally reach a high consensus with detecting and managing the non-cooperative decision makers. The DAD-LSDM model is feasible and efficient in practice for the intuitionistic fuzzy LSDM scenarios. [ABSTRACT FROM AUTHOR]

Corals are crucial animals as they support a large part of marine life. The automatic classification of corals species based on underwater images is important as it can help experts to track and detect threatened and vulnerable coral species. However, this classification is complicated due to the nature of coral underwater images and the fact that current underwater coral datasets are unrealistic as they contain only texture images, while the images taken by autonomous underwater vehicles show the complete coral structure. The objective of this paper is two-fold. The first is to build a dataset that is representative of the problem of classifying underwater coral images, the StructureRSMAS dataset. The second is to build a classifier capable of resolving the real problem of classifying corals, based either on texture or structure images. We have achieved this by using a two-level classifier composed of three ResNet models. The first level recognizes whether the input image is a texture or a structure image. Then, the second level identifies the coral species. To do this, we have used a known texture dataset, RSMAS, and StructureRSMAS. • Creation of a new coral structure dataset. • Study the performance of promising CNNs in the classification of coral images. • Analyse data augmentation on the performance of the coral classification models. • Evaluate image enhancement techniques on the performance of the models. • Creation of a two-level model able to classify any coral image, texture or structure. [ABSTRACT FROM AUTHOR]

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve "Stolby" (Krasnoyarsk, Russia). [ABSTRACT FROM AUTHOR]

The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data—likely to contain noise and imperfections—are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k‐nearest neighbors rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data—which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context are investigated. This includes a brief overview of Smart Data, current and future trends for the k‐nearest neighbor algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data‐ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k‐nearest neighbor algorithm to obtain Smart/Quality Data for a high‐quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analyzed. This article is categorized under:Technologies > Data PreprocessingFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Classification The k‐nearest neighbors algorithm plays a key role to cope with Big Data by transforming it into Smart data that is free of redundant information, noise and/or missing values. Gleaning quality data is essential for a correct data mining process that will uncover valuable insights. [ABSTRACT FROM AUTHOR]

The article presents a case study of patients who were diagnosed with histological pattern of colonic metastases resulting from primary cervical carcinoma. Clinical cases involved patients with metastatic colonic tumors who underwent series of examinations including radiotherapy and brachytherapy. Results of histopathological examination show a squamous cell epithelial carcinoma in three patients while the remaining patient was diagnosed with adenocarcinoma.