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Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, feature weighting is used to develop an effective computer-aided diagnosis system for breast cancer. Feature weighting is employed because it boosts the classification performance more as compared to feature subset selection. Specifically, a wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously. The selection of hidden neurons and backpropagation training algorithms are used as parameters of neural networks. The performance of the proposed approach is evaluated on three breast cancer datasets. The data is initially normalized using tanh method to remove the effects of dominant features and outliers. The results show that the proposed wrapper method has a better ability to attain higher accuracy as compared to the existing techniques. The obtained high classification performance validates the work which has the potential for becoming an alternative to the other well-known techniques.
Twórcy
  • Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India
  • Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India
  • Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India
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Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-407a1872-8cab-4b54-a7f8-49fc0425fb17
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