An algorithm acceleration framework for correlation-based feature selection

Xuefeng Yan; Yuqing Zhang; Arif Ali Khan

doi:10.1051/matecconf/202133607011

All issues

Volume 336 (2021)

MATEC Web Conf., 336 (2021) 07011

References

Open Access

Issue		MATEC Web Conf. Volume 336, 2021 2020 2^nd International Conference on Computer Science Communication and Network Security (CSCNS2020)


Article Number		07011
Number of page(s)		7
Section		Intelligence Algorithms and Application
DOI		https://doi.org/10.1051/matecconf/202133607011
Published online		15 February 2021

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[1] Jie Cai, Jiawei Luo, Shulin Wang, Sheng Yang. Feature selection in machine learning: A new perspective[J]. NEUROCOMPUTING, 2018, 300(jul.26):70-79. [Google Scholar]

[2] Chen Z, Wu C, Zhang Y, et al. Feature selection with redundancy-complementariness dispersion[J]. Knowledge-Based Systems, 2015, 89(NOV.):203-217. [Google Scholar]

[3] Zeng Z, Zhang H, Zhang R, et al. A novel feature selection method considering feature interaction[J]. Pattern Recognition, 2015, 48(8):2656-2666. [Google Scholar]

[4] Zhang Jinghong. Kernel density estimation entropy of mixed data and fast greedy feature selection algorithm [D]. Zhejiang University, 2017 [Google Scholar]

[5] C.E. Shannon, A mathematical theory of communication, ACM SIGMOBILE Mobile Comput. Commun. Rev. 5 (1) (2001) 3-55. [Google Scholar]

[6] H. Peng, F. Long, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell. 27 (8) (2005) 1226-1238. [Google Scholar]

[7] Gao W, Hu L, Zhang P, et al. Feature selection considering the composition of feature relevancy[J]. Pattern Recognition Letters, 2018, 112(SEP.1):70-74. [Google Scholar]

[8] M. Jureczko and L. Madeyski, “Towards identifying software project clusters with regard to defect prediction,” in Proc. 6th Int.Conf. Predictive Models Softw. Eng., 2010, Art. no. 9. [Google Scholar]