SCIENCE CHINA Information Sciences, Volume 63, Issue 1: 112103(2020) https://doi.org/10.1007/s11432-019-2633-y

## On some aspects of minimum redundancy maximum relevance feature selection

• ReceivedApr 25, 2019
• AcceptedAug 21, 2019
• PublishedDec 24, 2019
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### Abstract

The feature selection is an important challenge in many areas of machine learning because it plays a crucial role in the interpretations of machine-driven decisions. There are various approaches to the feature selection problem and methods based on the information theory comprise an important group. Here, the minimum redundancy maximum relevance (mRMR) feature selection is undoubtedly the most popular one with widespread application. In this paper, we prove in contrast to an existing finding that the mRMR is not equivalent to Max-Dependency criterion for first-order incremental feature selection. We present another form of equivalence leading to a generalization of mRMR feature selection. Additionally, we compare several feature selection methods based on mRMR, Max-Dependency, and feature ranking, employing different measures of dependency. The results on high-dimensional real-world datasets show that the distance correlation is the suitable measure for dependency-based feature selection methods. The results also indicate that the Max-Dependency incremental algorithm combined with distance correlation appears to be a promising feature selection approach.

### Acknowledgment

This work was supported by Slovak Research and Development Agency (Grant No. APVV-16-0211).

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• Figure 1

(Color online) The average $F_1$ score of four classifiers (NB, SVC, RF, kNN) on eight high-dimensional datasets. Comparison of multiple FS approaches. (a) Alon; (b) Tian; (c) Gordon; (d) Golub; (e) Burczynski; (f) Pomeroy; (g) Singh; (h) Chowdary.

• Figure 2

(Color online) The highest $F_1$ score of four classifiers (NB, SVC, RF, kNN) on eight high-dimensional datasets. Comparison of multiple FS approaches. (a) Alon; (b) Tian; (c) Gordon; (d) Golub; (e) Burczynski; (f) Pomeroy; (g) Singh; (h) Chowdary.

• Table 1   Dataset to compare mRMR and Max-Dependency algorithms
 $X_1$ $X_2$ $X_3$ Y $X_1$ $X_2$ $X_3$ Y 1 0 0 0 0 9 0 0 1 0 2 0 0 0 0 10 0 0 1 0 3 0 1 0 1 11 0 1 1 1 4 0 1 0 1 12 0 1 1 1 5 1 0 0 1 13 1 0 1 1 6 1 0 0 1 14 1 0 1 1 7 1 1 0 0 15 1 1 1 0 8 2 1 0 0 16 2 1 1 0
• Table 2   Contingency tables for counterexample to Theorem
 $(X_1,~X_2)$ $Y=0$ $Y=1$ $\Sigma$ $(X_1,~X_3)$ $Y=0$ $Y=1$ $\Sigma$ (0, 0) 4 0 4 (0, 0) 2 2 4 (1, 0) 0 4 4 (1, 0) 1 2 3 (2, 0) 0 0 0 (2, 0) 1 0 1 (0, 1) 0 4 4 (0, 1) 2 2 4 (1, 1) 2 0 2 (1, 1) 1 2 3 (2, 1) 2 0 2 (2, 1) 1 0 1 $\Sigma$ 8 8 16 $\Sigma$ 8 8 16
• Table 3   Characteristics of datasets used in this study
 Dataset (Source) Number of samples Number of features Number of Class 0 Number of Class 1 Alon [20] 62 2000 40 22 Tian [21] 173 12625 36 137 Gordon [22] 181 12533 94 87 Golub [23] 72 7129 47 25 Burczynski [24] 127 22283 85 42 Pomeroy [25] 60 7128 39 21 Singh [26] 102 12600 52 50 Chowdary [27] 104 22283 62 42
• Table 4   Contingency table for Gordon dataset
 (v12113, v3333, v3249) $Y=0$ $Y=1$ $\sum$ (0,1,0) 17 0 17 (0,1,2) 4 0 4 (0,2,0) 9 0 9 (0,2,2) 0 7 7 (1,2,0) 0 1 1 (1,2,1) 0 5 5 (1,2,2) 0 11 11 (2,1,2) 1 0 1 (2,2,0) 0 1 1 (2,2,1) 0 23 23 (2,2,2) 0 102 102 $\sum$ 31 150 181
• Table 5   Summary of results of prediction performance for different FS methods
 WTL Mean rank Standardized mean Standardized median mRMR/MI 183/55/114 5.4 0.44 0.41 mRMR/chi2 174/44/134 5.9 0.33 0.41 mRMR/Pear 249/51/52 3.4 0.7 0.73 mRMR/Spea 222/46/84 4.3 0.59 0.59 mRMR/dCor 234/53/65 3.9 0.63 0.63 MD/MI 33/9/310 10.8 $-$1.54 $-$1.53 MD/chi2 19/12/321 11.2 $-$1.86 $-$1.97 MD/dCor 249/28/75 3.8 0.64 0.81 Rank/MI 146/28/178 7.0 0.11 0.26 Rank/chi2 130/38/184 7.4 0.04 0.17 Rank/dCor 134/38/180 7.2 0.12 0.25 ReliefF 123/30/199 7.7 $-$0.21 0.01
• Table 6   Generalization of mRMR. Mean rank of maximum and average $F_1$ score versus parameter $\lambda$
 Parameter $\lambda$ 0 0.25 0.5 0.75 1 1.25 1.5 1.75 2 $F_1$ score MAX 7.25 5.81 5.44 4.19 4.38 4.69 3.63 4.06 5.56 $F_1$ score AVG 6.88 5.38 4.5 3.81 4.06 3.94 4.69 5.13 6.63
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