A multiple association-based unsupervised feature selection algorithm for mixed data sets

Author:

Taha AymanORCID,Hadi Ali S.ORCID,Cosgrave Bernard,McKeever Susan

Funder

Horizon 2020

Horizon 2020 Framework Programme

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference60 articles.

1. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection;Ang;IEEE/ACM Transactions on Computational Biology and Bioinformatics,2015

2. Arai, H., Maung, C., Xu, K., & Schweitzer, H. (2016). Unsupervised feature selection by heuristic search with provable bounds on suboptimality. In Proceedings of the AAAI conference on artificial intelligence (pp. 666–672).

3. L’analyse des données: l’analyse des correspondances;Benzécri,1973

4. Blasius, J., & Greenacre, M. J. (1994). Computation of Correspondence Analysis. In Correspondence analysis in the social sciences (pp. 53–75). London.

5. Conditional likelihood maximisation: A unifying framework for information theoretic feature selection;Brown;Journal of Machine Learning Research,2012

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