A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction Multiple classifier system (MCS) has become a successful alternative for improving classification performance. However, studies have shown inconsistent results for different MCSs, and it is often difficult to predict which MCS algorithm works the best on a particular problem. We believe that the two crucial steps of MCS  base classifier generation and multiple classifier combination, need to be designed coordinately to produce robust results. In this work, we show that for different testing instances, better classifiers may be trained from different subdomains of training instances including, for example, neighboring instances of the testing instance, or even instances far away from the testing instance. To utilize this intuition, we propose Individualized Classifier Ensemble (ICE). ICE groups training data into overlapping clusters, builds a classifier for each cluster, and then associates each training instance to the topperforming models while taking into account model types and frequency. In testing, ICE finds the k most similar training instances for a testing instance, then predicts class label of the testing instance by averaging the prediction from models associated with these training instances. Evaluation results on 49 benchmarks show that ICE has a stable improvement on a significant proportion of datasets over existing MCS methods. ICE provides a novel choice of utilizing internal patterns among instances to improve classification, and can be easily combined with various classification models and applied to many application domains.

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.