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Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time. [ABSTRACT FROM AUTHOR]

In multi-instance learning, instances are organized into bags, and a bag is labeled positive if it contains at least one positive instance, and negative otherwise; the labels of the individual instances are not given. The task is to learn a classifier from this limited information. While the original task description involved learning an instance classifier, in the literature the task is often interpreted as learning a bag classifier. Depending on which of these two interpretations is used, it is more natural to evaluate classifiers according to how well they predict, respectively, instance labels or bag labels. In the literature, however, the two interpretations are often mixed, or the intended interpretation is left implicit. In this paper, we investigate the difference between bag-level and instance-level accuracy, both analytically and empirically. We show that there is a substantial difference between these two, and better performance on one does not necessarily imply better performance on the other. It is therefore useful to clearly distinguish the two settings, and always use the evaluation criterion most relevant for the task at hand. We show experimentally that the same conclusions hold for area under the ROC curve. [ABSTRACT FROM AUTHOR]

Different from previous imputation methods which impute missing values in the incomplete samples by using the information in the complete samples, this paper proposes a Date-drive Incremental imputation Model, DIM for short, which uses all available information in the data set to impute missing values economically, effectively, orderly, and iteratively. To this end, we propose a scoring rule to rank the missing features by taking into account both the economical criterion and the effective imputation information. The economical criterion takes both the imputation cost and the discriminative ability of the feature into account, while the effective imputation information enables to use all observed information in the data set including the imputed missing values to impute the left missing values. During the imputation process, our DIM first detects the neednot-impute samples for reducing the imputation cost and noise, and then selects the missing features with the top rank to impute first. The imputation process orderly imputes the missing features until all missing values are imputed or the imputation cost is exhausted. Experimental results on UCI data sets demonstrated the advantages of our proposed DIM, compared to the comparison methods, in terms of prediction accuracy and classification accuracy. [ABSTRACT FROM AUTHOR]

Sparse representation (SR) has been widely studied and successfully applied to many areas of computer science in recent years. However, whether sparsity is essential to improve the classification performance is still an open question. Some studies reveal that it is the collaborative representation (CR) rather than SR that truly improves the classification performance. In this paper, the advantage of CR is further investigated and exploited, and a CR-based coding method is proposed. This method improves the classification performance by applying CR to the traditional nearest subspace (NS) method. Compared to the other NS method which codes the test sample on each class separately, the proposed method employs all samples to code the test sample collaboratively and preserves the subspace structure at the same time. The test sample is then classified to the class with the smallest representation error. Besides, a corresponding dictionary learning algorithm is also proposed so that the coding can be conducted on a dictionary learned from the training dataset. Since analytical solutions for coding and dictionary learning have been derived, our algorithm can be implemented efficiently. Experiments are conducted on seven face databases and the USPS handwritten digit database, and the results show that the proposed algorithm outperforms many state-of-the-art coding methods and dictionary learning methods, which demonstrates the power brought by CR. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are one the most challenging fields in computer science and networking in particular with the exposition of Internet of things. The energy management is a crucial issue on wireless sensor network since it has a real influence on then network lifetime. Since such networks are used for many sensitive applications, they need to guarantee some quality of service requirements as continuous connectivity or continuous monitoring or balanced energy consumption over all the network nodes. In this paper, we suggest an algorithm which monitors the energy consumption of all nodes (in relation with their main activity: communication). We present an activity model of the network and evaluate how the remaining energy behaves. Our main objective is to predict (after a learning step) the energy consumption of all the nodes. Then, various techniques could be used to find a cure to such troubles (wake up sleeping neighbor nodes, increasing radio ranges of some neighbor nodes, etc). Simulations have shown interesting results, and large-scale evaluation has to be undertaken to confirm the early results. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines. [ABSTRACT FROM PUBLISHER]

Research focus increases rapidly on recent years in mining imbalanced data set, because of its challenge and its extensive application on the real world. A dataset is said to be imbalance, if categories of the classification attribute is not evenly represented. A fine balanced dataset is an important source for the classifiers to define the best prediction model. All the existing classifiers are inclined to perform poor on the imbalanced datasets. The reason for this is, all the classifiers seek to optimize their overall accuracy not by considering the relative distribution of each class. Hence, it is very essential to go for well balanced dataset for classification. In this paper, the comprehensive Enhanced Minority Oversampling TEchnique (EMOTE) is proposed to improve the performance of the classifier by balancing the dataset. The key idea of the proposed method is to balance the dataset by tuning the misclassified instances of the minority classes into correctly classified instances through oversampling their nearest neighbor. To investigate the performance of the proposed model, different oversampling and under sampling methods inclusive of the well known method SMOTE (Synthetic Minority Oversampling TEchnique) are considered. Various imbalanced datasets from the UCI machine learning repository are considered for experiments The experimental results shows that, the proposed method EMOTE outperformed the other methods in balancing the dataset. In addition to this it is also proved that, the classifier is able to effectively improve its performance on the dataset which is generated by EMOTE. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c -class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought. [ABSTRACT FROM AUTHOR]

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weighted online sequential extreme learning machine (WOS-ELM), voting based weighted online sequential extreme learning machine (VWOS-ELM) and weighted online sequential extreme learning machine with kernels (WOS-ELMK), etc. handle the imbalanced learning effectively. One of our recent works class-specific extreme learning machine (CS-ELM) uses class-specific regularization and has been shown to perform better for imbalanced learning. This work proposes a novel online sequential class-specific extreme learning machine (OSCSELM), which is a variant of CS-ELM. OSCSELM supports online learning technique in both chunk-by-chunk and one-by-one learning mode. It targets to handle the class imbalance problem for both small and larger datasets. The proposed work has less computational complexity in contrast with WOS-ELM for imbalanced learning. The proposed method is assessed by utilizing benchmark real-world imbalanced datasets. Experimental results illustrate the effectiveness of the proposed approach as it outperforms the other methods for imbalanced learning. [ABSTRACT FROM AUTHOR]

This article discusses a study on designing a fake news detection system using machine learning models. The study found that the Decision Tree model was the most efficient in accurately detecting fake news, with an accuracy of 99.64%. The article emphasizes the importance of detecting fake news and provides a detailed methodology for the study. The research aims to raise awareness about fake news and contribute to its eradication in society. [Extracted from the article]

With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles’ content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short- text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers. [ABSTRACT FROM AUTHOR]

Simple Summary: Technological advancements have led to modern DNA sequencing methods, capable of generating large amounts of data describing the microorganisms that live in samples taken from the environment. Metagenomics, the field that studies the different genomes within these samples, is becoming increasingly popular, as it has many real-world applications, such as the discovery of new antibiotics, personalized medicine, forensics, and many more. From a computer science point of view, it is interesting to see how these large volumes of data can be processed efficiently to accurately identify (classify) the microorganisms from the input DNA data. This scoping review aims to give an insight into the existing state of the art computational methods for processing metagenomic data through the prism of machine learning, data science, and big data. We provide an overview of the state of the art metagenomic classification methods, as well as the challenges researchers face when tackling this complex problem. The end goal of this review is to help researchers be up to date with current trends, as well as identify opportunities for further research and improvements. Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries' search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement. [ABSTRACT FROM AUTHOR]

With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases. [ABSTRACT FROM PUBLISHER]

Since their inception, learning techniques under the reservoir computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches, specially deep neural networks. Among them, different flavors of echo state networks have attracted many stares through time, mainly due to the simplicity and computational efficiency of their learning algorithm. However, these advantages do not compensate for the fact that echo state networks remain as black-box models whose decisions cannot be easily explained to the general audience. This issue is even more involved for multi-layered (also referred to as deep) echo state networks, whose more complex hierarchical structure hinders even further the explainability of their internals to users without expertise in machine learning or even computer science. This lack of explainability can jeopardize the widespread adoption of these models in certain domains where accountability and understandability of machine learning models is a must (e.g., medical diagnosis, social politics). This work addresses this issue by conducting an explainability study of echo state networks when applied to learning tasks with time series, image and video data. Among these tasks, we stress on the latter one (video classification) which, to the best of our knowledge, has never been tackled before with echo state networks in the related literature. Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models, namely potential memory, temporal patterns and pixel absence effect. Potential memory addresses questions related to the effect of the reservoir size in the capability of the model to store temporal information, whereas temporal patterns unveil the recurrent relationships captured by the model over time. Finally, pixel absence effect attempts at evaluating the effect of the absence of a given pixel when the echo state network model is used for image and video classification. The benefits of the proposed suite of techniques are showcased over three different domains of applicability: time series modeling, image and, for the first time in the related literature, video classification. The obtained results reveal that the proposed techniques not only allow for an informed understanding of the way these models work, but also serve as diagnostic tools capable of detecting issues inherited from data (e.g., presence of hidden bias). [ABSTRACT FROM AUTHOR]

Recently, human action recognition (HAR) has become an important focus of computer science research because of its applications in surveillance, robotics, sentiment analysis, and other areas. Human activity classification is a time-consuming operation, especially when photos are cluttered, and the background is unclear. In addition, conventional machine learning models fail to achieve robust performance due to the increasing number of activities. Comparatively, deep learning (DL) approaches help to automatically learn and describe the necessary features of the input data with low manual work and robust discriminant abilities. In addition, a preprocessing stage is included in this study's HAR utilizing hyperparameter tuned DL (HAR-HPTDL) model that removes undesired background and improves the quality of the input. The model also implements a bidirectional long short-term memory model as a feature extractor, the sparrow search algorithm to tune the hyperparameters, and a SoftMax layer for the effective classification of human actions. In addition, the curse of dimensionality can be overcome via entropy-based feature reduction and Chi square-based feature selection. Based on a variety of measures, the HAR-HPTDL methodology has been put through its paces with other published techniques. The results indicate that the HAR-HPTDL technique outperforms current state-of-the-art techniques in simulations. The outcome of this work demonstrates that an HAR-HPTDL model may achieve comparable or even superior recognition accuracy of 0.949 than the prior best deep classifier(s) on all databases with proper parameter optimization. [ABSTRACT FROM AUTHOR]

Background and Purpose: One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine learning algorithm. Methods: Natural language processing combined with a machine learning algorithm was used to predict acuity levels based on electronic health record data. Results: The algorithm was able to predict acuity relatively well. A main challenge was discordance among nurse raters of acuity in generating a gold standard of acuity before applying the machine learning algorithm. Conclusions: This pilot study tested applying machine learning techniques to acuity measurement and yielded a moderate level of performance. Higher agreement among the gold standard may yield higher performance in future studies. [ABSTRACT FROM AUTHOR]

Biological data sets are increasingly becoming information-dense, making it effective to use a computer science-based analysis. We used convolution neural networks (CNN) and the specific CNN architecture Unet to study sponge behavior over time. We analyzed a large time series of hourly high-resolution still images of a marine sponge, Suberites concinnus (Demospongiae, Suberitidae) captured between 2012 and 2015 using the NEPTUNE seafloor cabled observatory, off the west coast of Vancouver Island, Canada. We applied semantic segmentation with the Unet architecture with some modifications, including adapting parts of the architecture to be more applicable to three-channel images (RGB). Some alterations that made this model successful were the use of a dice-loss coefficient, Adam optimizer and a dropout function after each convolutional layer which provided losses, accuracies and dice scores of up to 0.03, 0.98 and 0.97, respectively. The model was tested with five-fold cross-validation. This study is a first step towards analyzing trends in the behavior of a demosponge in an environment that experiences severe seasonal and inter-annual changes in climate. The end objective is to correlate changes in sponge size (activity) over seasons and years with environmental variables collected from the same observatory platform. Our work provides a roadmap for others who seek to cross the interdisciplinary boundaries between biology and computer science. [ABSTRACT FROM AUTHOR]