An improved relief feature selection algorithm based on Monte-Carlo tree search

Zheng, Jianyang; Zhu, Hexing; Chang, Fangfang; Liu, Yunlong

doi:10.1080/21642583.2019.1661312

Cited by 6 publications

(3 citation statements)

References 11 publications

(14 reference statements)

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“…• Relief algorithm considers the correlation between features, and feature weights are used to select the features to classify. Despite the ease with which the relief technique calculates classification weights, the results can be influenced by noise, which can lead to mistakes in the subset of features acquired [21]. Relief was originally proposed by Kira and Rendell [22].…”

Section: Figure 2 Gene Filter Methods Flowchartmentioning

confidence: 99%

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

AlMazrua

Alshamlan

2022

IEEE Access

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In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection is much needed to solve the high dimensionality issue in microarray data, small sample size, and noisy. The best way to classify cancer is to select those genes that hold the most informative ones, and this process contributes significantly to the classification performance of microarrays. In this survey, we comprehensively studied hybrid selection methods proposed since 2017 that may be used for comparison to several other algorithms proposed for gene selection in cancer classification in the past and looked to see if there are any challenges future authors that need to be discussed.

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Section: Figure 2 Gene Filter Methods Flowchartmentioning

confidence: 99%

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

AlMazrua

Alshamlan

2022

IEEE Access

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“…In the regression case, the fitness of BSO was substituted from the accuracy of the KNN classifier to the mean square error of the KNN regressor. The reward function of Q Learning only differed in minor sign modification from its original paper; • For MCTS_RreliefF, as the ReliefF algorithm was used to implement classification on multiclass outputs feature selection problem, we changed it into RreliefF algorithm; the other framework in the paper remained the same, including most parameter settings in [38];…”

Section: Comparison Methods and Metricsmentioning

confidence: 99%

Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search

Liu

Guo

2021

Entropy

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This paper investigates the Nested Monte Carlo Tree Search (NMCTS) for feature selection on regression tasks. NMCTS starts out with an empty subset and uses search results of lower nesting level simulation. Level 0 is based on random moves until the path reaches the leaf node. In order to accomplish feature selection on the regression task, the Gamma test is introduced to play the role of the reward function at the end of the simulation. The concept Vratio of the Gamma test is also combined with the original UCT-tuned1 and the design of stopping conditions in the selection and simulation phases. The proposed GNMCTS method was tested on seven numeric datasets and compared with six other feature selection methods. It shows better performance than the vanilla MCTS framework and maintains the relevant information in the original feature space. The experimental results demonstrate that GNMCTS is a robust and effective tool for feature selection. It can accomplish the task well in a reasonable computation budget.

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“…The algorithm used MCTS to learn sub-optimal feature trees, by simultaneously partitioning the search space into different localities. The MCTS-based method to improve the relief algorithm is proposed in Reference [ 34 ]. The authors used the exhaustive tree with relief (a feature selection algorithm) as an evaluator to select the best feature subset.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets

Chaudhry

Yasir

Asghar

et al. 2020

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The complexity and high dimensionality are the inherent concerns of big data. The role of feature selection has gained prime importance to cope with the issue by reducing dimensionality of datasets. The compromise between the maximum classification accuracy and the minimum dimensions is as yet an unsolved puzzle. Recently, Monte Carlo Tree Search (MCTS)-based techniques have been invented that have attained great success in feature selection by constructing a binary feature selection tree and efficiently focusing on the most valuable features in the features space. However, one challenging problem associated with such approaches is a tradeoff between the tree search and the number of simulations. In a limited number of simulations, the tree might not meet the sufficient depth, thus inducing biasness towards randomness in feature subset selection. In this paper, a new algorithm for feature selection is proposed where multiple feature selection trees are built iteratively in a recursive fashion. The state space of every successor feature selection tree is less than its predecessor, thus increasing the impact of tree search in selecting best features, keeping the MCTS simulations fixed. In this study, experiments are performed on 16 benchmark datasets for validation purposes. We also compare the performance with state-of-the-art methods in literature both in terms of classification accuracy and the feature selection ratio.

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An improved relief feature selection algorithm based on Monte-Carlo tree search

Cited by 6 publications

References 11 publications

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search

Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets

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