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MOTiFS: Monte Carlo Tree Search Based Feature Selection.

Muhammad Umar Chaudhry1, Jee-Hyong Lee1

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.

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Summary
This summary is machine-generated.

This study introduces a new Monte Carlo Tree Search (MCTS) method for efficient feature selection in machine learning. The approach optimizes feature subsets to improve classification accuracy on large datasets.

Keywords:
MOTiFSMonte Carlo Tree Search (MCTS)dimensionality reductionfeature selectionheuristic feature selectionwrapper

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Area of Science:

  • Machine Learning
  • Data Science
  • Computational Intelligence

Background:

  • Increasing dataset size and complexity necessitate feature reduction for effective machine learning.
  • High classification accuracy often requires identifying optimal feature subsets.

Purpose of the Study:

  • To present a novel and efficient Monte Carlo Tree Search (MCTS) algorithm for optimal feature subset selection.
  • To enhance classification accuracy by reducing the number of features required.

Main Methods:

  • The proposed method utilizes Monte Carlo Tree Search (MCTS) to explore the feature space.
  • The algorithm incrementally builds a search tree, evaluating feature subsets using classifier accuracy as a reward signal.
  • It combines tree search policies with random sampling for efficient exploration.

Main Results:

  • The MCTS-based feature selection method was validated on numerous benchmark datasets.
  • Experimental results demonstrated the efficiency and effectiveness of the proposed approach.
  • Performance was compared against existing methods, showing superior results.

Conclusions:

  • The novel MCTS algorithm effectively identifies optimal feature subsets for machine learning.
  • This method offers a superior approach to feature selection, improving classification accuracy.
  • The technique is efficient and effective for handling large and complex datasets.