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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...

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Detalles Bibliográficos
Autores principales: Chaudhry, Muhammad Umar, Lee, Jee-Hyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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.
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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
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