Swarm Intelligence Approach Based on Adaptive ELM Classifier with ICGA Selection for Microarray Gene Expression and Cancer Classification

摘要: The aim of this research study is based on efficient gene selection and classification microarray data analysis using hybrid machine learning algorithms. beginning technology has enabled the researchers to quickly measure position thousands genes expressed in an organic/biological tissue samples a solitary experiment. One important applications classify their expression representation, identify numerous type cancer. Cancer group diseases which set cells shows uncontrolled growth, instance that interrupts upon destroys nearby tissues spreading other locations body via lymph or blood. becomes one major disease current scenario. DNA microarrays turn out be effectual tool utilized molecular biology cancer diagnosis. Microarrays can measured establish relative quantity mRNAs two additional for thousands/several at same time. As superiority technique become exactly analysis/identifying suitable assessment various open issues. In field medical sciences multi-category play role types according expression. need been indispensible, because numbers victims are increasing steadily identified by recent years. To perform proposed combination Integer-Coded Genetic Algorithm (ICGA) Artificial Bee Colony algorithm (ABC), coupled with Adaptive Extreme Learning Machine (AELM), used classification. ICGA ABC AELM classifier chose optimal results handle sparse sample imbalance. performance approach evaluated compared existing methods.