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Related Experiment Video

Updated: Nov 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets.

Muhammad Umar Chaudhry1,2, Muhammad Yasir3, Muhammad Nabeel Asghar4

  • 1AiHawks, Multan 60000, Pakistan.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel iterative feature selection algorithm to address big data challenges. The new method enhances classification accuracy and reduces dimensions by building multiple feature selection trees, improving upon existing Monte Carlo Tree Search techniques.

Keywords:
Monte Carlo Tree Search (MCTS)R-MOTiFSdimensionality reductionfeature selectionheuristic feature selection

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Big data presents challenges due to complexity and high dimensionality.
  • Feature selection is crucial for dimensionality reduction and improving model performance.
  • Existing Monte Carlo Tree Search (MCTS) methods for feature selection face a tradeoff between search depth and simulation count.

Purpose of the Study:

  • To propose a new iterative algorithm for feature selection that overcomes limitations of existing MCTS-based methods.
  • To improve the balance between classification accuracy and dimensionality reduction.
  • To enhance the efficiency of feature selection in high-dimensional big data.

Main Methods:

  • A novel algorithm is proposed that iteratively builds multiple feature selection trees in a recursive manner.
  • Each successor tree has a reduced state space, intensifying the impact of tree search.
  • The number of Monte Carlo Tree Search (MCTS) simulations is kept fixed while improving search effectiveness.

Main Results:

  • Experiments were conducted on 16 benchmark datasets to validate the proposed algorithm.
  • The algorithm was compared against state-of-the-art methods in terms of classification accuracy and feature selection ratio.
  • The iterative approach demonstrated improved performance in selecting optimal feature subsets.

Conclusions:

  • The proposed iterative multi-tree approach effectively addresses the limitations of single-tree MCTS methods in feature selection.
  • This method offers a promising solution for high-dimensional big data by optimizing the feature selection process.
  • The algorithm achieves a better compromise between classification accuracy and reduced dimensionality, outperforming existing techniques.