Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches
Zhaomin Yao, Gancheng Zhu, Jingwei Too, Meiyu Duan, Zhiguo Wang
Abstract
OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.
Topics & Concepts
Computer scienceSwarm intelligenceFeature selectionSwarm behaviourFeature (linguistics)Data miningKey (lock)Artificial intelligenceDimension (graph theory)Selection (genetic algorithm)Machine learningParticle swarm optimizationMathematicsComputer securityPure mathematicsPhilosophyLinguisticsGene expression and cancer classificationMachine Learning in BioinformaticsBioinformatics and Genomic Networks