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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]

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]

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]