scholarly journals MOTiFS: Monte Carlo Tree Search Based Feature Selection

Entropy ◽  
2018 ◽  
Vol 20 (5) ◽  
pp. 385 ◽  
Author(s):  
Muhammad Chaudhry ◽  
Jee-Hyong Lee
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Tree Search ◽  
Monte Carlo Tree Search

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

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1093
Author(s):  
Muhammad Umar Chaudhry ◽  
Muhammad Yasir ◽  
Muhammad Nabeel Asghar ◽  
Jee-Hyong Lee
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Recursive Algorithm ◽  
Feature Subset Selection ◽  
Great Success ◽  
Feature Subset ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
The Impact

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.

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

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1331
Author(s):  
Ying Li ◽  
Guohe Li ◽  
Lingun Guo
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Feature Space ◽  
Relevant Information ◽  
Tree Search ◽  
Gamma Test ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Original Feature

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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