Feature subset selection using improved binary gravitational search algorithm
作者： Esmat Rashedi , Hossein Nezamabadi-pour
DOI: 10.3233/IFS-130807
关键词: Genetic algorithm 、 Content-based image retrieval 、 Feature selection 、 Feature (machine learning) 、 Search algorithm 、 Data mining 、 Binary number 、 Pattern recognition 、 Selection (genetic algorithm) 、 Mathematics 、 Pattern recognition (psychology) 、 Artificial intelligence
摘要: Feature selection is one of the important activities in various fields such as computer vision and pattern recognition. In this paper, an improved version binary gravitational search algorithm BGSA proposed used a tool to select best subset features with goal improving classification accuracy. By enhancing transfer function, we give ability overcome stagnation situation. This allows explore larger group possibilities avoid stagnation. To evaluate IBGSA, some well known datasets accuracy CBIR systems are experienced. Results compared those original BGSA, genetic GA, particle swarm optimization BPSO, electromagnetic-like mechanism. Comparative results confirm effectiveness IBGSA feature selection.
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