One-class classifier ensemble pruning and weighting with firefly algorithm

This paper introduces a novel technique for forming efficient one-class classifier ensembles. It combines an ensemble pruning algorithm with weighted classifier fusion module. The ensemble pruning is realized as a search problem and implemented through a swarm intelligence approach. A firefly algorithm is selected as the framework for managing the process of reducing the size of the classifier pool. Input classifiers are coded as population members. The interactions between fireflies are realized through the consistency measure, which describes the effectiveness of individual one-class classifiers. A new pairwise diversity measure, based on calculating the intersections between spherical one-class classifiers, is used for controlling the movements of fireflies. With this, we indirectly implement a multi-objective optimization, as selected classifiers have at the same time high individual accuracy and are mutually diverse. The fireflies form groups and for each group the best representative is selected – thus realizing the pruning task. Additionally, a classifier weight calculation scheme based on the brightness of fireflies is applied for weighted fusion. Experimental analysis, backed-up with statistical tests, proves the quality of the proposed method and its ability to outperform state-of-the-art algorithms for selecting one-class classifiers for the classification committees.