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Rule-Based Classification Analysis Using Entropy Distribution
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 Title & Authors
Rule-Based Classification Analysis Using Entropy Distribution
Lee, Jung-Jin; Park, Hae-Ki;
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 Abstract
Rule-based classification analysis is widely used for massive datamining because it is easy to understand and its algorithm is uncomplicated. In this classification analysis, majority vote of rules or weighted combination of rules using their supports are frequently used in order to combine rules. We propose a method to combine rules by using the multinomial distribution in this paper. Iterative proportional fitting algorithm is used to estimate the multinomial distribution which maximizes entropy constrained on rules' support. Simulation experiments show that this method can compete with other well known classification models in the case of two similar populations.
 Keywords
Rule-based classification analysis;maximum entropy distribution;iterative proportional fitting algorithm;
 Language
Korean
 Cited by
 References
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