Abstract：As an instance based classifier, kNN has many computational and store requirements. Meanwhile, the poor performance of kNN classifier is caused by the imbalance distribution of training data. Aiming at these defects of kNN classifier, a technique, combining feature selection and condensing, is proposed to reduce the time cost and the space of kNN classifier. The proposed algorithm is divided into two steps. Firstly, several traditional methods of feature selection are combined to form features for each class. Then, redundant cases are removed by combination of class features contained in samples with Condensing algorithm. Experimental results indicate when the sample set acquired by the proposed method is used as training set, the classifier saves the time cost and the space dramatically, and the performance of the kNN classifier is improved because noisy data are removed from the training set.
郝秀兰，陶晓鹏，王述云，徐和祥，胡运发. 基于特征选择及Condensing技术的文本取样*[J]. 模式识别与人工智能, 2009, 22(5): 709-717.
HAO Xiu-Lan, TAO Xiao-Peng, WANG Shu-Yun, XU He-Xiang, HU Yun-Fa. Documents Sampling Based on Feature Selection and Condensing Techniques. , 2009, 22(5): 709-717.
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2009, Vol. 22 
