Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper’s goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research directions
Semi-supervised Multiple Classifier Systems: Background and Research Directions
ROLI, FABIO
2005-01-01
Abstract
Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper’s goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research directions
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