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

Abstract

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.

Comparative Analysis of Rainfall-Runoff Modeling Using Support Vector Machines for Two Dams in Uttarakhand

“…It can be defined as a non-parametric test which is based on some trial-and-error method. The Gamma test is a non-linear modeling and analysis tool to test the relationship between input and output variables on the numerical dataset [4]. The main principle in this test is that if there exists two input quantities m and m' that lie close to each other in an input space then the respective output quantities n and n' should also be close to each other in their given output space.…”