Empirical Study of Individual Feature Evaluators and Cutting Criteria for Feature Selection in Classification
作者： Antonio Arauzo-Azofra , José L. Aznarte M. , José M. Benítez
关键词: Machine learning 、 Artificial neural network 、 Process (engineering) 、 Feature selection 、 Feature extraction 、 Empirical research 、 Modular programming 、 Feature (computer vision) 、 Selection (genetic algorithm) 、 Computer science 、 Data mining 、 Artificial intelligence
摘要: The use of feature selection can improve accuracy, efficiency, applicability and understandability a learning process its resulting model. For this reason, many methods automatic have been developed. By using modularization process, paper evaluates wide spectrum these methods. considered are created by combination different criteria individual evaluation modules. These commonly used because their low running time. After carrying out thorough empirical study the most interesting identified some recommendations about which method should be under conditions provided.
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