Abstract
In order to select correlated and relevant features in a feature selection, several filter methods adopt a symmetric uncertainty as one of the feature ranking measures. In this paper, we introduce a fluctuation into the increasing order of the symmetric uncertainty for the consistency-based feature selection algorithms. Here, the fluctuation is an operation of transforming the sorted sequence of features to a new sequence of features. Then, we compare the selected features by the algorithms with a fluctuation with those without fluctuations.
The author would like to express thanks for support by Grant-in-Aid for Scientific Research 17H00762, 16H02870 and 16H01743 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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Notes
- 1.
NIPS 2003 Workshop on Feature Extraction and Feature Selection Challenge. http://clopinet.com/isabelle/Projects/NIPS2003/#challenge.
- 2.
WCCI 2004 Performance Prediction Challenge. http://clopinet.com/isabelle/Projects/modelselect/datasets/.
- 3.
NCBI, National Center for Biotechnology Information. http://www.ncbi.gov/genome/FLU/.
- 4.
LIBSVM: A Library for Support Vector Machine: https://www.csie.ntu.edu.tw/~cjlin/libsvm/.
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Shimamura, S., Hirata, K. (2019). Introducing Fluctuation into Increasing Order of Symmetric Uncertainty for Consistency-Based Feature Selection. In: Gopal, T., Watada, J. (eds) Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science(), vol 11436. Springer, Cham. https://doi.org/10.1007/978-3-030-14812-6_34
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