A Family of the Online Distance-Based Classifiers

In this paper a family of algorithms for the online learning and classification is considered. These algorithms work in rounds, where at each round a new instance is given and the algorithm makes a prediction. After the true class of the instance is revealed, the learning algorithm updates its internal hypothesis. The proposed algorithms are based on fuzzy C-means clustering followed by calculation of distances between cluster centroids and the incoming instance for which the class label is to be predicted. Simple distance-based classifiers thus obtained serve as basic classifiers for the implemented rotation forest kernel. The proposed approach is validated experimentally. Experiment results show that proposed classifiers perform well against competitive approaches.

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