A hybrid genetic algorithm for classification
作者： James D. Kelly , Lawrence Davis
DOI:
关键词: Hybrid algorithm 、 Population-based incremental learning 、 Data mining 、 FSA-Red Algorithm 、 Genetic algorithm 、 k-nearest neighbors algorithm 、 Nearest-neighbor chain algorithm 、 Training set 、 Meta-optimization 、 Best bin first 、 Linde–Buzo–Gray algorithm 、 Computer science 、 Pattern recognition 、 Artificial intelligence
摘要: In this paper we describe a method for hybridizing genetic algorithm and k nearest neighbors classification algorithm. We use the training data set to learn real-valued weights associated with individual attributes in set. classify new records based on their weighted distance from members of applied our hybrid three test cases. Classification results obtained exceed performance all
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