Modeling Interestingness of Streaming Classification Rules as a Classification Problem
Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathered information from the domain, the new classification rules are induced. Therefore, these rules stream through time and are so called streaming classification rules. In this paper, an interactive classification rules’ interestingness learning algorithm (ICRIL) is developed to automatically label the classification rules either as “interesting” or “uninteresting” with limited user interaction. In our study, VFFP (Voting Fuzzified Feature Projections), a feature projection based incremental classification algorithm, is also developed in the framework of ICRIL. The concept description learned by the VFFP is the interestingness concept of streaming classification rules.
KeywordsLinguistic Term Classification Rule Training Instance Nominal Feature User Participation
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