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MOTiFS: Monte Carlo Tree Search Based Feature Selection
Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512904/ https://www.ncbi.nlm.nih.gov/pubmed/33265475 http://dx.doi.org/10.3390/e20050385 |
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author | Chaudhry, Muhammad Umar Lee, Jee-Hyong |
author_facet | Chaudhry, Muhammad Umar Lee, Jee-Hyong |
author_sort | Chaudhry, Muhammad Umar |
collection | PubMed |
description | Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the tree is incrementally built by adding nodes representing the inclusion or exclusion of the features in the feature space. Every iteration leads to a feature subset following the tree and default policies. The accuracy of the classifier on the feature subset is used as the reward and propagated backwards to update the tree. Finally, the subset with the highest reward is chosen as the best feature subset. The efficiency and effectiveness of the proposed method is validated by experimenting on many benchmark datasets. The results are also compared with significant methods in the literature, which demonstrates the superiority of the proposed method. |
format | Online Article Text |
id | pubmed-7512904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75129042020-11-09 MOTiFS: Monte Carlo Tree Search Based Feature Selection Chaudhry, Muhammad Umar Lee, Jee-Hyong Entropy (Basel) Article Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the tree is incrementally built by adding nodes representing the inclusion or exclusion of the features in the feature space. Every iteration leads to a feature subset following the tree and default policies. The accuracy of the classifier on the feature subset is used as the reward and propagated backwards to update the tree. Finally, the subset with the highest reward is chosen as the best feature subset. The efficiency and effectiveness of the proposed method is validated by experimenting on many benchmark datasets. The results are also compared with significant methods in the literature, which demonstrates the superiority of the proposed method. MDPI 2018-05-20 /pmc/articles/PMC7512904/ /pubmed/33265475 http://dx.doi.org/10.3390/e20050385 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chaudhry, Muhammad Umar Lee, Jee-Hyong MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title | MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title_full | MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title_fullStr | MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title_full_unstemmed | MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title_short | MOTiFS: Monte Carlo Tree Search Based Feature Selection |
title_sort | motifs: monte carlo tree search based feature selection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512904/ https://www.ncbi.nlm.nih.gov/pubmed/33265475 http://dx.doi.org/10.3390/e20050385 |
work_keys_str_mv | AT chaudhrymuhammadumar motifsmontecarlotreesearchbasedfeatureselection AT leejeehyong motifsmontecarlotreesearchbasedfeatureselection |