Modeling interestingness of streaming classification rules as a classification problem
Author
Aydın, Tolga
Güvenir, Halil Altay
Date
2005-06Source Title
Turkish Symposium on Artificial Intelligence and Neural Networks TAINN, 2005
Publisher
Springer
Pages
168 - 176
Language
English
Type
Conference PaperItem Usage Stats
148
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104
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Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
Keywords
Classification (of information)Data processing
Learning algorithms
Logic programming
User interfaces
Incremental classification algorithms
Interestingness learning algorithm (ICRIL)
Streaming classification
Problem solving
Permalink
http://hdl.handle.net/11693/27255Published Version (Please cite this version)
http://dx.doi.org/10.1007/11803089_20Collections
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