imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over-and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub https://github.com/scikit-learn-contrib/imbalanced-learn.

In many real world classification tasks, all data classes are not represented equally. This problem, known also as the curse of class imbalanced in data sets, has a potential impact in the training procedure of a classifier by learning a model that will be biased in favor of the majority class. In this work at hand, an under-sampling approach is proposed, which leverages the usage of a Naive Bayes classifier, in order to select the most informative instances from the available training set, based on a random initial selection. The method starts by learning a Naive Bayes classification model on a small stratified initial training set. Afterwards, it iteratively teaches its base model with the instances that the model is most uncertain about and retrains it until some criteria are satisfied. The overall performance of the proposed method has been scrutinized through a rigorous experimental procedure, being tested using six multimodal data sets, as well as another forty-four standard benchmark data sets. The empirical results indicate that the proposed undersampling method achieves comparable classification performance in contrast to other resampling techniques, regarding several proper metrics and having performed a suitable statistical testing procedure.

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One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.

A variety of methods have been developed in order to tackle a classication problem in the eld of decision support systems. A hybrid prediction scheme which combines several classiers, rather than selecting a single robust method, is a good alternative solution. In order to address this issue, we have provided an ensemble of classiers to create a hybrid decision support system. This method based on stacking variant methodology that combines strong ensembles to make predictions.The presented hybrid method has been compared with other knownensembles. The experiments conducted on several standard benchmark datasets showed that the proposed scheme gives promising results in terms of accuracy in most of the cases.