一种快速的特征选择框架和方法

A Fast Feature Selection Framework and Method

针对特征选择过程中准确率和计算效率不平衡问题,提出了一种快速特征选择框架(FFFS).基于该框架,使用最小冗余最大相关方法(MRMR)选择候选特征,借助序列前向选择方法(SFS)验证性能,并通过限定迭代次数提高计算性能.与MRMR、SFS和混合序列浮动前向选择算法(FDHSFFS)的对比实验结果表明,提出的快速特征选择算法MRMR-SFS能在预测准确率和计算效率之间取得较好的平衡.

Aiming at the imbalance between accuracy and computational efficiency in feature selection,a fast feature selection framework(FFFS)is proposed.Based on this framework,a fast feature selection algorithm,MRMR-SFS,is proposed.The minimum redundancy maximum relevance(MRMR)method is used to select the candidate features,and sequential forward selection(SFS)method is used to verify the performance of the candidate features as well.It improves the calculation efficiency by limiting the number of iterations.Comparison experiments with the MRMR,SFS and a filter-dominating hybrid sequential floating forward selection algorithms demonstrate that MRMR-SFS can balance the accuracy and computational efficiency well.