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Applied Intelligence

, Volume 37, Issue 3, pp 357–376 | Cite as

Optimizing the modified fuzzy ant-miner for efficient medical diagnosis

  • Thannob Aribarg
  • Siriporn Supratid
  • Chidchanok Lursinsap
Article

Abstract

The advantage of efficient searches belonging to ant-miner over several other approaches leads to prominent achievements on rules mining. Fuzzy ant-miner, an extension of the ant-miner provides a fuzzy mining framework for the automatic extraction of fuzzy rules from labeled numerical data. However, it is easily trapped in local optimal, especially when it applies to medical cases, where real world accuracy is elusive; and the interpretation and integration of medical knowledge is necessary. In order to relieve such a local optimal difficulty, this paper proposes OMFAM which applies simulated annealing to optimize fuzzy set parameters associated with a modified fuzzy ant-miner (MFAM). MFAM employs attributes and training case weighting. The proposed method, OMFAM was experimented with six critical medical cases for developing efficient medical diagnosis systems. The performance measurement relates to accuracy as well as interpretability of the mined rules. The performance of the OMFAM is compared with such references as MFAM, fuzzy ant-miner (FAM), and other classification methods. At last, it indicates the superiority of the OMFAM algorithm over the others.

Keywords

Ant-miner Fuzzy logic Simulated annealing Adaptive neuro-fuzzy inference system Multi-class support vector machine 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Thannob Aribarg
    • 1
  • Siriporn Supratid
    • 1
  • Chidchanok Lursinsap
    • 2
  1. 1.Department of Information TechnologyRangsit UniversityPathumthaniThailand
  2. 2.Advanced Virtual and Intelligent Computing Lab, Department of Mathematics, Faculty of ScienceChulalongkorn UniversityBangkokThailand

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