Efficient Feature Selection Algorithm Based on Population Random Search with Adaptive Memory Strategies

Статья в сборнике трудов конференции

The effectiveness of classifier training methods depends significantly on the number of features that describe a dataset to be classified. This research proposesanew approach tofeatureselection that combinesrandomandheuristic search strategies. A solution is represented as a binary vector whose size is determined by the number of features in a dataset. New solutions are generated randomly using normal and uniform distributions. The heuristic underlying the proposed approach is formulated as follows: the chance for a feature to be includedintothenextgenerationisproportionaltothefrequencyofitsoccurrence in the previous best solutions. For feature selection, we have used the algorithm with a fuzzy classifier. The method is tested on several datasets from the KEEL repository. Comparison with analogs is presented. To compare feature selection algorithms, we found the values their efficiency criterion. This criterion reflects theaccuracyoftheclassificationandthespeedoffindingtheappropriatefeatures.

Библиографическая запись: Hodashinsky, I. Efficient Feature Selection Algorithm Based on Population Random Search with Adaptive Memory Strategies / I. Hodashinsky, K. Sarin, A. Slezkin // Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). - Sochi: Springer, 2019. - AISC 874. - pp. 1-10. - DOI: 10.1007/978-3-030-01818-4_32

Конференция:

• Intelligent Information Technologies for Industry
• Чехия, Moravskoslezský Kraj, Ostrava, 2-07 декабря 2019,
• Международная

Издательство:

Springer Nature Switzerland AG

Швейцария, Kanton Basel-Stadt, Basel