Adaptive Particle Swarm Optimization based Credentialed Extreme Learning Machine Classifier (APSO-CELMC) for High Dimensional Datasets
M.Praveena1, V. Jaiganesh2

1M.Praveena, MCA,M.Phil, Asst.Professor, Department of Computer Science, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore-49. Tamilnadu, India.

2V. Jaiganesh, MCA, M.Phil, MBA, Ph.D., Professor, Department of PG and Research in Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore – 641 048. Tamilnadu, India.

Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 29 June 2020 | PP: 157-163 | Volume-8 Issue-10S2 August 2019 | Retrieval Number: J102908810S19/2019©BEIESP | DOI: 10.35940/ijitee.J1029.08810S19

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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

Keywords: Machine Learning, Feed Forward Neural Network, Extreme Learning Machine, Optimization, Particle Swarm, Swarm Intelligence, High Dimensional Datasets.
Scope of the Article: Machine/ Deep Learning with IoT & IoE