Feature Selection Approach based on Mutual Information and Partial Least Squares

Qiang Shi; Jian Tang; Li Jie Zhao

doi:10.4028/www.scientific.net/AMR.875-877.2025

Paper Titles

Nonstationary Response of Optimal Controlled Stochastic Van Der Pol Oscillator
p.2000

A Novel Image Segmentation Enhance Technology for Motion Human Body
p.2006

Higher Order Sliding Mode Control for the Tracking Trajectory - The Twisting Algorithm Applied to the Vehicle Model
p.2014

Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data
p.2020

Feature Selection Approach based on Mutual Information and Partial Least Squares
p.2025

The Model Reference Adaptive Control of the DC Electric Drive System
p.2030

A New Design of Variable Traction Tracked System
p.2036

Monotone Constraint Model Applied to the Reliability Growth Evaluation of Electromechanical Products
p.2042

Magnetic Circuit Design and Analysis Used in Magnetic Sealing Drive Device
p.2047

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 875-877Feature Selection Approach based on Mutual...

Feature Selection Approach based on Mutual Information and Partial Least Squares

Abstract:

Feature selection technology can improve the modeling accuracy and reduce model’s complexity, especially for the high dimensional spectral data. Aim at this problem, feature selection approach based on mutual information (MI) and partial least square (PLS) is proposed in this paper. MI values between features and responsible variable are calculated, and the threshold value using to select final features is optimal selected based on PLS algorithm. The numbers of the latent values of the PLS and the threshold value of MI are selected according the modeling performance simultaneously. The experimental results based on the near-infrared spectrum show that the proposed approach has better performance.