Induction of multiple criteria optimal classification rules for biological and medical data

Han-Lin Li*, Ming Hsien Chen

*此作品的通信作者

研究成果: Article同行評審

21 引文 斯高帕斯(Scopus)

摘要

To induce critical classification rules from observed data is a major task in biological and medical research. A classification rule is considered to be useful if it is optimal and simultaneously satisfies three criteria: is highly accurate, has a high rate of support, and is highly compact. However, current classification methods, such as rough set theory, neural networks, ID3, etc., may only induce feasible rules instead of optimal rules. In addition, the rules found by current methods may only satisfy one of the three criteria. This study proposes a multi-criteria model to induce optimal classification rules with better rates of accuracy, support and compactness. A linear multi-objective programming model for inducing classification rules is formulated. Two practical data sets, one of HSV patients results and another of European barn swallows, are tested. The results illustrate that the proposed method can induce better rules than current methods.

原文English
頁(從 - 到)42-52
頁數11
期刊Computers in Biology and Medicine
38
發行號1
DOIs
出版狀態Published - 1月 2008

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