Adaptive particle swarm optimization based credentialed extreme learning machine classifier (APSO-CELMC) for high dimensional datasets

Abstract

Data mining is a key research field in the computer science research arena. Feature selection is performed once the dataset got cleansed. Optimization algorithms are considered to be helpful for the feature selection task. Also the obtained suitable features will contribute considerably for the classifier. Machine learning classifiers are comparatively performing better than that of traditional data mining classification algorithms. In this part of research work an adaptive particle swarm optimization algorithm is employed in order to perform feature selection task. Extreme learning machine classifier is added with credential weights. Twenty datasets are taken for performance analysis. From the obtained results it is evident that Adaptive Particle Swarm Optimization based Credentialed Extreme Learning Machine Classifier (APSO-CELMC) performs better in terms of predictive accuracy and time taken for classification.