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In an earlier paper, we proposed a new negative correlation learning (NCL) algorithm for classification ensembles, called AdaBoost.NC, which has significantly better performance than the standard AdaBoost and other NCL algorithms on many benchmark data sets with low computation cost. In this paper, we give deeper insight into this algorithm from both theoretical and experimental aspects to understand...
This paper proposes a new negative correlation learning (NCL) algorithm, called AdaBoost.NC, which uses an ambiguity term derived theoretically for classification ensembles to introduce diversity explicitly. All existing NCL algorithms, such as CELS and NCCD, and their theoretical backgrounds were studied in the regression context. We focus on classification problems in this paper. First, we study...
Class imbalance learning is an important research area in machine learning, where instances in some classes heavily outnumber the instances in other classes. This unbalanced class distribution causes performance degradation. Some ensemble solutions have been proposed for the class imbalance problem. Diversity has been proved to be an influential aspect in ensemble learning, which describes the degree...
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