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Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms

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Applications of Intelligent Optimization in Biology and Medicine

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 96))

Abstract

Breast cancer is the most frequently diagnosed cancer in women worldwide and the leading cause of cancer death among females. Currently the most effective method for early detection and screening of breast abnormalities is mammography. Computer aided design (CAD) systems are used to assist radiologists in better classification of tumor in a mammography as benign or malignant. Ensemble classifier construction has received considerable attention in the recent years. In the modeling of classifier ensemble, many researchers believe that the success of classifier ensembles only when classifier members present diversity among themselves. The most widely used ensemble creation techniques are focused on incorporating the concept of diversity with the construction of different features subsets or selection of the most diverse components from initial classifiers pool. Therefore the motivation of this work is to propose a CAD system using a novel classification approach based on feature selection and static classifier selection schemes.

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Authors and Affiliations

  1. Labged Laboratory, Annaba, Algeria

    Soraya Cheriguene, Nabiha Azizi, Nawel Zemmal & Nadir Farah

  2. Computer Science department, Badji Mokhtar University, PO BOX 12, 23000, Annaba, Algeria

    Soraya Cheriguene, Nabiha Azizi, Nawel Zemmal & Nadir Farah

  3. Computer Science department, LRS Laboratory, Badji Mokhtar University, PO BOX 12, 23000, Annaba, Algeria

    Hayet Djellali

  4. Department of Computer Science, Bengal College of Engineering & Technology, Durgapur, India

    Nilanjan Dey

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  1. Soraya Cheriguene
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  2. Nabiha Azizi
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  3. Nawel Zemmal
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  4. Nilanjan Dey
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  5. Hayet Djellali
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  6. Nadir Farah
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Correspondence to Nabiha Azizi .

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  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Information Systems and Computing, Brunel University, London, United Kingdom

    Crina Grosan

  3. Faculty of Computers and information, Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

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Cheriguene, S., Azizi, N., Zemmal, N., Dey, N., Djellali, H., Farah, N. (2016). Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms . In: Hassanien, AE., Grosan, C., Fahmy Tolba, M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-21212-8_13

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