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An Effective Feature Selection method using Monte Carlo Search

Authors:

Muhammad Umar Chaudhry,

Sang-Wook Kim,

Jee-Hyong LeeAuthors Info & Claims

RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems

Pages 44 - 45

https://doi.org/10.1145/3129676.3130240

Published: 20 September 2017 Publication History

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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.

References

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Brahim, A. B., and Limam, M., 2016. A hybrid feature selection method based on instance learning and cooperative subset search. Pattern Recognition Letters. 69 (2016), 28--34.

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Browne, C., Powley, E., Whitehouse, D., Lucas, S., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S., 2012. IEEE Transactions on Computational Intelligence and AI in Games. 4 (Mar. 2012).

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Paul, S., and Das, S., 2015. Simultaneous feature selection and weighting - An evolutionary multi-objective optimization approach. Pattern Recognition Letters. 65 (2015), 51--59.

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G, M., and Derakhshi, M. R., 2016. Feature Selection using Forest Optimization Algorithm. Pattern recognition. 60 (2016), 121--12

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Index Terms

An Effective Feature Selection method using Monte Carlo Search
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection

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Published In

RACS '17: Proceedings of the International Conference on Research in Adaptive and Convergent Systems

September 2017

324 pages

ISBN:9781450350273

DOI:10.1145/3129676

Conference Chairs:
Katarzyna Grygiel
Jagiellonian University, Poland
,
Tomas Cerny
Baylor University, USA
,
Program Chairs:
Sung-Ryul Kim
Konkuk University, Korea
,
Wei Wang
San Diego State University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 20 September 2017

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SIGAPP
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RACS '17: International Conference on Research in Adaptive and Convergent Systems

September 20 - 23, 2017

Krakow, Poland

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RACS '17 Paper Acceptance Rate 48 of 207 submissions, 23%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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A Feature Selection Method Using Hierarchical Clustering

Dimensionality Reduction: Is Feature Selection More Effective Than Random Selection?

Feature Selection Using Distance from Classification Boundary and Monte Carlo Simulation

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