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pyDML is an open-source python library that provides a wide range of distance metric learning algorithms. Distance metric learning can be useful to improve similarity learning algorithms, such as the nearest neighbors classifier, and also has other applications, like dimensionality reduction. The pyDML package currently provides more than 20 algorithms, which can be categorized, according to their purpose, in: dimensionality reduction algorithms, algorithms to improve nearest neighbors or nearest centroids classifiers, information theory based algorithms or kernel based algorithms, among others. In addition, the library also provides some utilities for the visualization of classifier regions, parameter tuning and a stats website with the performance of the implemented algorithms. The package relies on the scipy ecosystem, it is fully compatible with scikit-learn, and is distributed under GPLv3 license. Source code and documentation can be found at https://github.com/jlsuarezdiaz/pyDML. [ABSTRACT FROM AUTHOR]

Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier. [ABSTRACT FROM AUTHOR]

Classification with imbalanced data-sets supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real applications. In this paper, we will work with fuzzy rule based classification systems using a preprocessing step in order to deal with the class imbalance. Our aim is to analyze the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets by means of the application of an adaptive inference system with parametric conjunction operators. Our results shows empirically that the use of the this parametric conjunction operators implies a higher performance for all data-sets with different imbalanced ratios. [Copyright &y& Elsevier]

In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis. [Copyright &y& Elsevier]

Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning. • We present an overview of recent and iconic bagging and boosting based algorithms. • We list their software tools available online and features. • We perform a performance study, extracting some lessons learned. • Some perspectives on ensemble learning are discussed. • Some opportunities and future fields are examined for ensembles in machine learning. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a novel binocular image approach that makes the detection model focus on the area of interest. • We built a low cost symmetric dual camera system to compute the disparity map and exploit that information to improve the selection of candidate regions in the input frames. • The proposed approach reduces the number of false positives in the test videos by 49.47%. Abstract Object detection models have known important improvements in the recent years. The state-of-the art detectors are end-to-end Convolutional Neural Network based models that reach good mean average precisions, around 73%, on benchmarks of high quality images. However, these models still produce a large number of false positives in low quality videos such as, surveillance videos. This paper proposes a novel image fusion approach to make the detection model focus on the area of interest where the action is more likely to happen in the scene. We propose building a low cost symmetric dual camera system to compute the disparity map and exploit this information to improve the selection of candidate regions from the input frames. From our results, the proposed approach not only reduces the number of false positives but also improves the overall performance of the detection model which make it appropriate for object detection in surveillance videos. [ABSTRACT FROM AUTHOR]

Highlights • Study the performance of promising CNNs in the classification of coral texture images. • Analyze different types of transfer learning. • Analyze data augmentation on the performance of the coral classification model. • Experimental results outperform state-of-the-art methods needing human intervention. • Generalize the best approach to other coral texture datasets. Graphical abstract Abstract The recognition of coral species based on underwater texture images poses a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: (1) datasets do not include information about the global structure of the coral; (2) several species of coral have very similar characteristics; and (3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reasons, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have focused on the current small datasets and analyzed (1) several Convolutional Neural Network (CNN) architectures, (2) data augmentation techniques and (3) transfer learning approaches. We have achieved the state-of-the art accuracies using different variations of ResNet on the two small coral texture datasets, EILAT and RSMAS. [ABSTRACT FROM AUTHOR]

In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, noise filtering, which removes noisy examples from the training data, is one of the most used techniques. This paper proposes a new noise filtering method that combines several filtering strategies in order to increase the accuracy of the classification algorithms used after the filtering process. The filtering is based on the fusion of the predictions of several classifiers used to detect the presence of noise. We translate the idea behind multiple classifier systems, where the information gathered from different models is combined, to noise filtering. In this way, we consider the combination of classifiers instead of using only one to detect noise. Additionally, the proposed method follows an iterative noise filtering scheme that allows us to avoid the usage of detected noisy examples in each new iteration of the filtering process. Finally, we introduce a noisy score to control the filtering sensitivity, in such a way that the amount of noisy examples removed in each iteration can be adapted to the necessities of the practitioner. The first two strategies (use of multiple classifiers and iterative filtering) are used to improve the filtering accuracy, whereas the last one (the noisy score) controls the level of conservation of the filter removing potentially noisy examples. The validity of the proposed method is studied in an exhaustive experimental study. We compare the new filtering method against several state-of-the-art methods to deal with datasets with class noise and study their efficacy in three classifiers with different sensitivity to noise. [ABSTRACT FROM AUTHOR]

Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. In this manner, symbolic data mining algorithms can be applied over continuous data and the representation of information is simplified, making it more concise and specific. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found. However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners. Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed. With the intention of alleviating these problems, this paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective. From the theoretical perspective, we develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date. Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers, and a large number of data sets. The results of their performances measured in terms of accuracy, number of intervals, and inconsistency have been verified by means of nonparametric statistical tests. Additionally, a set of discretizers are highlighted as the best performing ones. [ABSTRACT FROM AUTHOR]

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]

The nearest neighbor classifier is one of the most used and well-known techniques for performing recognition tasks. It has also demonstrated itself to be one of the most useful algorithms in data mining in spite of its simplicity. However, the nearest neighbor classifier suffers from several drawbacks such as high storage requirements, low efficiency in classification response, and low noise tolerance. These weaknesses have been the subject of study for many researchers and many solutions have been proposed. Among them, one of the most promising solutions consists of reducing the data used for establishing a classification rule (training data) by means of selecting relevant prototypes. Many prototype selection methods exist in the literature and the research in this area is still advancing. Different properties could be observed in the definition of them, but no formal categorization has been established yet. This paper provides a survey of the prototype selection methods proposed in the literature from a theoretical and empirical point of view. Considering a theoretical point of view, we propose a taxonomy based on the main characteristics presented in prototype selection and we analyze their advantages and drawbacks. Empirically, we conduct an experimental study involving different sizes of data sets for measuring their performance in terms of accuracy, reduction capabilities, and runtime. The results obtained by all the methods studied have been verified by nonparametric statistical tests. Several remarks, guidelines, and recommendations are made for the use of prototype selection for nearest neighbor classification. [ABSTRACT FROM AUTHOR]