Ensemble learning by means of a multiobjective optimization design approach for dealing with imbalanced data sets Abstract   Ensemble learning methods have already shown to be powerful techniques for creating classifiers. However, when dealing with realworld engineering problems, class imbalance is usually found. In such scenario, canonical machine learning algorithms may not present desirable solutions, and techniques for overcoming this problem must be used. In addition to using learning algorithms that alleviate the imbalance between classes, multiobjective optimization design (MOOD) approaches can be used to improve the prediction performance of ensembles of classifiers. This paper proposes a study of different MOOD approaches for ensemble learning. First, a taxonomy on multiobjective ensemble learning (MOEL) is proposed. In it, four types of existing approaches are defined: multiobjective ensemble member generation (MOEMG), multiobjective ensemble member selection (MOEMS), multiobjective ensemble member combination (MOEMC) and multiobjective ensemble member selection and combination (MOEMSC). Additionally, new approaches can be derived by combining the previous ones, such as multiobjective ensemble member generation and selection (MOEMGS), multiobjective ensemble member generation and combination (MOEMGC) and multiobjective ensemble member generation, selection and combination (MOEMGSC). With the given taxonomy, two experiments are conducted for (1) comparing the performance of the MOEL techniques for generating and aggregating base models on several imbalanced benchmark problems and (2) the performance of MOEL techniques against other machine learning techniques in a realworld imbalanced drinkingwater quality anomaly detection problem. Finally, results indicate that MOOD is able to improve the predictive performance of existing ensemble learning techniques.

MultiWindow Based Ensemble Learning for Classification of Imbalanced Streaming Data Imbalanced streaming data is widely existed in real world and has attracted much attention in recent years. Most studies focus on either imbalance data or streaming data; however, both imbalance data and streaming data are always accompanied in practice. In this paper, we propose a multiwindow based ensemble learning (MWEL as short) method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances and the ensemble classifier. The ensemble classifier consists of a set of latest subclassifiers, and instances each subclassifier trained on respectively. All subclassifiers are weighted before predicting new arriving instancexe2x80x99s class labels and new subclassifiers are trained if a precision is below a threshold. Extensive experiments on synthetic datasets and real world datasets demonstrate that the new approach can efficiently and efficiently classify imbalanced streaming data and outperform existing approaches.