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An effective feature selection method using Monte Carlo Search
- Chaudhry, Muhammad Umar;
- Kim, Sang-Wook;
- Lee, Jee-Hyong
Citations
SCOPUS
0초록
Feature selection is the challenging problem in the field of machine learning. The task is to identify the optimal feature subset by eliminating the redundant and irrelevant features from the dataset. The problem becomes more complicated when dealing with high-dimensional datasets. In this paper, we propose the novel technique based on Monte Carlo Tree Search (MCTS) to find the best feature subset to classify the dataset in hand. The effectiveness and validity of the proposed method is demonstrated by experimenting on many real world datasets.
키워드
Feature Selection; Heuristic Feature Selection; Monte Carlo Search; Artificial intelligence; Classification (of information); Learning systems; Monte Carlo methods; Feature selection methods; Feature subset; Heuristic features; High dimensional datasets; Monte Carlo tree search (MCTS); Novel techniques; Real-world datasets; Feature extraction
- 제목
- An effective feature selection method using Monte Carlo Search
- 저자
- Chaudhry, Muhammad Umar; Kim, Sang-Wook; Lee, Jee-Hyong
- 발행일
- 2017-09
- 유형
- Conference Paper
- 저널명
- Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017
- 권
- 2017-January
- 페이지
- 44 ~ 45