Efficient and Intelligent Feature Selection via Maximum Conditional Mutual Information for Microarray Data
Source abstract
Abstract
The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm’s superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence.
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Research context
OpenAlex topics: Gene expression and cancer classification · Face and Expression Recognition · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Network Information Management Division, Qingdao Agricultural University, Qingdao 266109, China
School of Science and Information Science, Qingdao Agricultural University, Qingdao 266109, China
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Funding
Natural Science Foundation of Shandong Province · the Action Plan Project for Rural Revitalization, Scientific and Technological Innovation of Shandong Province · he Key R&D Program of Shandong Province (Soft Science Project) · the Agricultural Improved Seed Project of Shandong Province · the Advanced Talents Foundation of Qingdao Agricultural University · the Crosswise Research Tasks of Qingdao Agricultural University
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