关键词:  , 二手车推荐, 分类, 非平衡数据, 过采样, 支持向量机

Abstract:  Due to the fact the used-car data have unbalanced characteristics, recommendation of used-cars boils down to unbalanced data classification problem and it can be solved with the unbalanced classification methods. In this paper, with the focus on reconstruction of the trainning data set and by an analysis of characteristics and deficiency of the SMOTE over-sampling method, we propose the Synthetic Minority Over-sampling Technique Filter, or SmoteFilter for short. It works by filtering the data generated by SMOTE over-sampling and reduces the noise in generated data. Based on support vector machine using data generated by SMOTE and SmoteFilter, the experimental study shows that SmoteFilter method has better effect on predicting accuracy of minority class than the SMOTE method, improving the prediction performance of vehicle recommendation.

Key words:  used-car recommendation, classification, imbalanced dataset, over-sampling, support vector machine