Filtering Feature Selection Algorithm based on Fusion Strategy
- Resource Type
- Conference
- Authors
- Xiao, Yufei; Liu, Tianhe
- Source
- 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA) Fully Actuated System Theory and Applications (CFASTA), 2023 2nd Conference on. :675-680 Jul, 2023
- Subject
- Aerospace
Robotics and Control Systems
Signal Processing and Analysis
Filtering
Diversity reception
Machine learning
Interference
Filtering algorithms
Feature extraction
Filtering theory
Feature selection
machine learning
relief-F
- Language
This paper mainly concerns filtering algorithms for feature selection of high-dimensional data in the field of machine learning. Feature selection implies that some of data is useless or even plays a negative role for machine learning, more specifically, there are redundant and invalid features among them. This paper applies fusion strategy for feature selection by combining different filtenng standards to obtain a new one. The basic architecture of step-by-step filtering is described in order to ensure the accuracy and efficiency of feature selection, upon which the Relief-F-MRMR filtering criterion and specific operation steps are designed. An illustrative example is provided to show the validity and advantage of the proposed approach.