Feature selection algorithms in classification problems: an experimental evaluation
作者： A. Salappa , M. Doumpos , C. Zopounidis
DOI: 10.1080/10556780600881910
关键词: Computer science 、 Machine learning 、 Algorithm 、 Artificial intelligence 、 Set (abstract data type) 、 Selection (genetic algorithm) 、 Variety (cybernetics) 、 Feature selection 、 Pattern recognition (psychology) 、 Data mining 、 Feature (machine learning) 、 Knowledge extraction 、 Reduction (complexity)
摘要: Feature selection (FS) is a significant topic for the development of efficient pattern recognition systems. FS refers to most appropriate subset features that describes (adequately) given classification task. The objective present paper perform thorough analysis performance and efficiency feature algorithms (FSAs). covers variety important issues with respect functionality FSAs, such as: (a) their ability identify relevant features, (b) models developed on reduced set (c) reduction in number (d) interactions between different FSAs techniques used develop model. considers methods.
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