@inproceedings{73d0edc753ba4a49a8454643f783ca51,
title = "An effective feature selection method using Monte Carlo Search",
abstract = "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.",
keywords = "Feature Selection, Heuristic Feature Selection, Monte Carlo Search",
author = "Chaudhry, \{Muhammad Umar\} and Kim, \{Sang Wook\} and Lee, \{Jee Hyong\}",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright is held by the owner/author(s).; 2017 International Conference on Research in Adaptive and Convergent Systems, RACS 2017 ; Conference date: 20-09-2017 Through 23-09-2017",
year = "2017",
month = sep,
day = "20",
doi = "10.1145/3129676.3130240",
language = "English",
series = "Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "44--45",
booktitle = "Proceedings of the 2017 Research in Adaptive and Convergent Systems, RACS 2017",
}