Nearest neighbor classification of categorical data by attributes weighting
作者：
Highlights：
• An effective solution for nearest neighbor classification on categorical data.
• Two global attribute-weighting approaches applied for categorical data classification.
• Two local attribute-weighting approaches applied for categorical data classification.
• Strong results of the new classifiers compared with the traditional kNN and the decision tree.
• Detailed analysis on the different behaviors of the various attribute-weighting methods.
摘要
•An effective solution for nearest neighbor classification on categorical data.•Two global attribute-weighting approaches applied for categorical data classification.•Two local attribute-weighting approaches applied for categorical data classification.•Strong results of the new classifiers compared with the traditional kNN and the decision tree.•Detailed analysis on the different behaviors of the various attribute-weighting methods.
论文关键词：Nearest neighbor classification,Categorical data,Distance measure,Projected subspace,Feature selection,Attribute weighting
论文评审过程：Available online 11 December 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.12.002
