You are in:Home/Publications/Inbarani HH, Jothi. G, Azar AT (2013). Hybrid Tolerance-PSO Based Supervised Feature Selection For Digital Mammogram Images. International Journal of Fuzzy System Applications (IJFSA), 3(4), 15-30. [Impact Factor: 1.65].

Dr. Assoc. Prof. Ahmad Taher Azar :: Publications:

Title:
Inbarani HH, Jothi. G, Azar AT (2013). Hybrid Tolerance-PSO Based Supervised Feature Selection For Digital Mammogram Images. International Journal of Fuzzy System Applications (IJFSA), 3(4), 15-30. [Impact Factor: 1.65].
Authors: G. Jothi, H. Hannah Inbarani, Ahmad Taher Azar,
Year: 2014
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Ahmad Taher Azar_hybrid-tolerance-rough-set_-pso-based-supervised-feature-selection-for-digital-mammogram-images.pdf
Supplementary materials Not Available
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 ap - plied 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.

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