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Hybrid Tolerance Rough Set: PSO Based Supervised Feature Selection for Digital Mammogram Images

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  • G. Jothi

    (Department of IT, Sona College of Technology (Autonomous), Salem, Tamil Nadu, India)

  • H. Hannah Inbarani

    (Department of Computer Science, Periyar University, Salem, Tamil Nadu, India)

  • Ahmad Taher Azar

    (Faculty of Computers and Information, Benha University, Benha, Egypt)

Abstract

Breast cancer is the most common malignant tumor found among young and middle aged women. Feature Selection is a process of selecting most enlightening features from the data set which preserves the original significance of the features following reduction. The traditional rough set method cannot be directly applied to deafening data. This is usually addressed by employing a discretization method, which can result in information loss. This paper proposes an approach based on the tolerance rough set model, which has the flair to deal with real-valued data whilst simultaneously retaining dataset semantics. In this paper, a novel supervised feature selection in mammogram images, using Tolerance Rough Set - PSO based Quick Reduct (STRSPSO-QR) and Tolerance Rough Set - PSO based Relative Reduct (STRSPSO-RR), is proposed. The results obtained using the proposed methods show an increase in the diagnostic accuracy.

Suggested Citation

  • G. Jothi & H. Hannah Inbarani & Ahmad Taher Azar, 2013. "Hybrid Tolerance Rough Set: PSO Based Supervised Feature Selection for Digital Mammogram Images," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 3(4), pages 15-30, October.
    • Handle: RePEc:igg:jfsa00:v:3:y:2013:i:4:p:15-30
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