A Feature Selection Algorithm Based on Equal Interval Division and Minimal-Redundancy–Maximal-Relevance
- 13 Downloads
Minimal-redundancy–maximal-relevance (mRMR) algorithm is a typical feature selection algorithm. To select the feature which has minimal redundancy with the selected features and maximal relevance with the class label, the objective function of mRMR subtracts the average value of mutual information between features from mutual information between features and the class label, and selects the feature with the maximum difference. However, the problem is that the feature with the maximum difference is not always the feature with minimal redundancy maximal relevance. To solve the problem, the objective function of mRMR is first analyzed and a constraint condition that determines whether the objective function can guarantee the effectiveness of the selected features is achieved. Then, for the case where the objective function is not accurate, an idea of equal interval division is proposed and combined with ranking to process the interval of mutual information between features and the class label, and that of the average value of mutual information between features. Finally, a feature selection algorithm based on equal interval division and minimal-redundancy–maximal-relevance (EID–mRMR) is proposed. To validate the performance of EID–mRMR, we compare it with several incremental feature selection algorithms based on mutual information and other feature selection algorithms. Experimental results demonstrate that the EID–mRMR algorithm can achieve better feature selection performance.
KeywordsMinimal-redundancy–maximal-relevance Equal interval division Mutual information Feature selection
This work was supported by the National Natural Science Foundation of China (61771334).
- 8.Fei T, Kraus D, Zoubir AM (2012) A hybrid relevance measure for feature selection and its application to underwater objects recognition. In: International conference on image processing, pp 97–100Google Scholar
- 18.Lewis DD (1992) Feature selection and feature extraction for text categorization. In: Proceedings of the workshop on speech and natural language, pp 212–217Google Scholar
- 22.Zhang Y, Ding C, Li T (2008) Gene selection algorithm by combining reliefF and mRMR. BMC Genom 9(2):1–10Google Scholar
- 25.UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 6 Mar 2018
- 26.ASU feature selection datasets. http://featureselection.asu.edu/datasets/. Accessed 6 Mar 2018
- 27.Fayyad U, Irani K (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of joint conference on artificial intelligence, pp 1022–1027Google Scholar
- 29.Zhao Z, Morstatter F, Sharma S, Alelyani S, Anand A, Liu H (2010) Advancing feature selection research. In: ASU feature selection repository, pp 1–28Google Scholar
- 36.Nguyen XV, Chan J, Romano S, Bailey J (2014) Effective global approaches for mutual information based feature selection. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, pp 512–521Google Scholar
- 42.Shishkin A, Bezzubtseva AA, Drutsa A, Shishkov I, Gladkikh E, Gusev G, Serdyukov P (2016) Efficient high order interaction aware feature selection based on conditional mutual information. In: Proceedings of the conference on advances in neural information processing systems, pp 4637–4645Google Scholar