由于二手车推荐的数据集具有非平衡特性，因此，二手车推荐可视为非平衡分类问题，可借助解决非平衡分类问题的方法来实现二手车推荐。本文对非平衡数据分类的数据集重构进行研究，通过分析合成少数类过采样方法（ Synthetic Minority Over-sampling Technique ， SMOTE）的特点与不足，提出合成少数类过采样过滤器方法（ Synthetic Minority Over-sampling Technique Filter ， SmoteFilter ），对SMOTE方法合成样本进行过滤，减少合成样本中的噪声数据，提高训练样本“质量”。使用支持向量机对SMOTE合成的数据和SmoteFilter合成的数据进行实验对比，结果表明SmoteFilter方法相较传统的SMOTE过采样方法，提高了二手车推荐中少数类的预测精度，提升了对二手车推荐的整体预测性能。%Due to the fact the used-car data have unbalanced characteristics , recommendation of used-cars boils down to unbal-anced 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 genera-ted 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 .
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