A Modified Firefly Algorithm with Support Vector Machine for Medical Data Classification

Abstract : Clinical information systems store a large amount of data in medical databases. In the use of medical dataset for diagnosis, the patient’s information is selectively collected and interpreted based on previous knowledge for detecting the existence of disorders. Feature selection is important and necessary data pre-processing step in medical data classification process. In this work, we propose a wrapper method for feature subset selection based on a binary version of the Firefly Algorithm combined with the SVM classifier, which tries to reduce the initial size of medical data and to select a set of relevant features for enhance the classification accuracy of SVM. The proposed method is evaluated on some medical dataset and compared with some well-known classifiers. The computational experiments show that the proposed method with optimized SVM parameters provides competitive results and finds high quality solutions.
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Conference papers
Abdelmalek Amine; Malek Mouhoub; Otmane Ait Mohamed; Bachir Djebbar. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-522, pp.232-243, 2018, Computational Intelligence and Its Applications. 〈10.1007/978-3-319-89743-1_21〉
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Submitted on : Wednesday, November 7, 2018 - 10:43:04 AM
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Brahim Sahmadi, Dalila Boughaci, Rekia Rahmani, Noura Sissani. A Modified Firefly Algorithm with Support Vector Machine for Medical Data Classification. Abdelmalek Amine; Malek Mouhoub; Otmane Ait Mohamed; Bachir Djebbar. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-522, pp.232-243, 2018, Computational Intelligence and Its Applications. 〈10.1007/978-3-319-89743-1_21〉. 〈hal-01913910〉

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