Feature Weighting

버전 1.0.1.4 (4.02 KB) 작성자: Dalwinder Singh

MATLAB Source Code for Feature Weighting with Ant Lion optimization

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다운로드 수: 89

업데이트 날짜: 2020/12/13

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In this code Ant Lion optimization is used to search the feature weights along with the optimal value of nearest neighbor parameter (i.e. k) simultaneously.

Run Main.m file for demo.

The feature weighting with ALO is implemented in FW_ALO.m file. Check this file for more details.

The source code and datasets are also available at GitHub: https://github.com/dalwindercheema/featweight

인용 양식

Singh, Dalwinder, and Birmohan Singh. “Hybridization of Feature Selection and Feature Weighting for High Dimensional Data.” Applied Intelligence, vol. 49, no. 4, Springer Science and Business Media LLC, Nov. 2018, pp. 1580–96, doi:10.1007/s10489-018-1348-2.

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MATLAB 릴리스 호환 정보

개발 환경: R2019b

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도움

받음: Ant Lion Optimizer (ALO)

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버전	게시됨	릴리스 정보
1.0.1.4	2020/12/13	Minor Change in Code	다운로드
1.0.1.3	2020/2/9	Description Update	다운로드
1.0.1.2	2020/2/9	Description Update	다운로드
1.0.1.1	2020/1/5	Description Update	다운로드
1.0.1	2020/1/4	Description updated	다운로드
1.0.0	2020/1/3		다운로드

MLA	Singh, Dalwinder, and Birmohan Singh. “Hybridization of Feature Selection and Feature Weighting for High Dimensional Data.” Applied Intelligence, vol. 49, no. 4, Springer Science and Business Media LLC, Nov. 2018, pp. 1580–96, doi:10.1007/s10489-018-1348-2.
APA	Singh, D., & Singh, B. (2018). Hybridization of feature selection and feature weighting for high dimensional data. Applied Intelligence, 49(4), 1580–1596. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs10489-018-1348-2
BibTeX	@article{Singh_2018, doi = {10.1007/s10489-018-1348-2}, url = {https://doi.org/10.1007%2Fs10489-018-1348-2}, year = 2018, month = {nov}, publisher = {Springer Science and Business Media {LLC}}, volume = {49}, number = {4}, pages = {1580--1596}, author = {Dalwinder Singh and Birmohan Singh}, title = {Hybridization of feature selection and feature weighting for high dimensional data}, journal = {Applied Intelligence} }