On evaluating the online local pool generation method for imbalance learning

Imbalanced problems are characterized by a disproportion between the number of samples from the classes in a classification problem. This difference in amount of examples may lead to a bias toward the majority class, hindering the recognition of the underrepresented minority class. Ensemble methods have been widely used for dealing with such problems, and have been shown to perform well on them. In this context, Dynamic Selection (DS) approaches, which perform the classification task on a local level, have been receiving some attention for their promising results. More specifically, the Frienemy Indecision Region Dynamic Ensemble Selection++ (FIRE-DES++) framework, which has yielded state-of-the-art results on imbalanced problems, use a data preprocessing technique for noise removal and a class-balanced neighborhood definition for coping with imbalanced datasets. A different DS-based approach proposed in a previous work, an online local pool generation method, generates on the fly locally accurate classifiers for labelling samples near the class borders. Though the local generation of the classifiers may reduce the impact of class imbalance on the performance of the technique, its suitability for imbalance learning was not yet evaluated. Thus, in this work we evaluate how well the online local pool generation method deals with imbalanced problems. We perform a comparative analysis with a baseline technique using three Dynamic Classifier Selection (DCS) techniques over 64 imbalanced datasets and four performance measures. We also evaluate the use of the preprocessing and balanced neighborhood definition steps from the FIRE-DES++ on the online scheme to assess their impact on the performance of the method. Moreover, we evaluate the online technique and its variants against seven state-of-the-art ensemble methods, including both static and DS approaches. Experimental results show that the approach of locally generating the classifiers is advantageous for imbalance learning, providing an improvement to the DCS techniques and yielding state-of-the-art results. Furthermore, the addition of the noise removal and the balanced neighborhood definition steps to the online scheme improved the overall results of the technique, which indicates the advantage of including such steps in DS-based techniques.