Filtering Feature Selection Algorithm based on Fusion Strategy
2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA)(2023)
摘要
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.
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关键词
Feature selection,machine learning,relief-F
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