A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems
作者： Ray R. Hashemi , Bruce A. Pearce , Ramin B. Arani , Willam G. Hinson , Merle G. Paule
DOI: 10.1007/978-1-4613-1461-5_9
关键词: Attention deficit disorder 、 Diagnostic system 、 Artificial intelligence 、 Rough set 、 Engineering 、 Genetic algorithm 、 Data mining 、 Machine learning 、 ID3 、 Hybrid system
摘要: A hybrid classification system is a composed of several intelligent techniques such that the inherent limitations one individual technique be compensated for by strengths another technique. In this paper, we investigate outline diagnostic Attention Deficit Disorder (ADD) in children. This uses Rough Sets (RS) and Modified (MRS) to induce rules from examples then our modified genetic algorithms globalize rules. Also, capability was compared with behavior (a) using RS, MRS, “dropping condition” approach, (b) Interactive Dichotomizer 3 (ID3) (c) basic algorithm.
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