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Codes and necessary datasets to reproduce the paper "McTwo: a two-step feature selection algorithm based on maximal information coefficient"

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McTwo-Evaluation

Codes and necessary datasets to reproduce the paper "McTwo: a two-step feature selection algorithm based on maximal information coefficient"

Long live open source.

Outline

Comparative Analysis

指标:

分类准确度

选择特征数

比较对象:

Wrapper algorithm(Class1):PAM & RRF&CFS

Filter algorithm(Class2):TRank & WRank & ROCRank

FCBF:外部交叉验证

效能评估:分类算法SVM & NBayes & Dtree & NN

衡量指标:Sn Sp Acc Avc

1.McOne vs McTwo

(1)Gas1和T1D的Acc(30次5-flod交叉验证)

(2)17个数据集的特征选择数和mAcc

2.McTwo vs Class1

(1)三元组(win/tie/loss)四种分类算法最大结果比较17个数据集上的mAcc

(2)EI比较模型复杂度和准确率

3.McTwo vs Class2

(1)挑选和McTwo所取特征数量一样的前p个特征用三元组法比较mAcc

4.外部交叉验证

(1)数据集ALL1 Gas1 Mye

(2)Class1:CFS FCBF McTwo PAM Rfe RRF

(3)Class2:McTwo RfeRank ROCRank TRank WRank

(4)Acc的框图

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Codes and necessary datasets to reproduce the paper "McTwo: a two-step feature selection algorithm based on maximal information coefficient"

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