HYBRIDIZATION OF PSO-ABC BASED ENSEMBLE CLASSIFICATION MODEL FOR HIGH DIMENSIONAL MEDICAL DATASETS

摘要

As the size and dimensionality of microarray datasets increase, it is vital to select essential features for data classification.Traditionally, ranking and selection measures are used to select the essential features from the high dimensional feature space.However, these measures are used to improve the data classification rate with limited number of instances and features space.Feature selection is one of the challenging issues for microarray datasets due to noise, sparsity and missing values. Traditionalfeature selection models such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)are used to select the highly weighted features for data classification. But these models require high computational memory andtime for data classification. In this paper, a hybrid PSO+ABC based feature selection model is designed and implemented onmicroarray disease datasets. Proposed hybrid feature selection model is applied on multiple classification models to improve thetrue positive rate and error rate for different dimensional datasets. Experimental results are simulated on different microarraydisease dataset and these results proved that the hybrid feature selection model has high true positive rate and minimal meansquared error rate compared to the traditional models.