Interactive Classification Using a Granule Network

Interactive Classification Using a Granule Network

Yan Zhao (University of Regina, Canada) and Yiyu Yao (University of Regina, Canada)
DOI: 10.4018/jcini.2007100107
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Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This article studies interactive classification at three levels: At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing (GrC) model is suggested for re-examining not only existing classification problems but also interactive classification problems. At the application level, an interactive classification system (ICS) using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.

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