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

Han-Lin Li*, Ming Hsien Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish
Pages (from-to)42-52
Number of pages11
JournalComputers in Biology and Medicine
Issue number1
StatePublished - 1 Jan 2008


  • Classification rules
  • Multiple criteria
  • Optimal

Fingerprint Dive into the research topics of 'Induction of multiple criteria optimal classification rules for biological and medical data'. Together they form a unique fingerprint.

Cite this