Abstract — The aim of this paper is to propose a hybrid classification algorithm based on particl... more Abstract — The aim of this paper is to propose a hybrid classification algorithm based on particle swarm optimization (PSO) to enhance the generalization performance of the Adaptive Boosting (AdaBoost) algorithm. AdaBoost enhances any given machine learning algorithm performance by producing some weak classifiers which requires more time and memory and may not give the best classification accuracy. For this purpose, We proposed PSO as a post optimization procedure for the resulted weak classifiers and removes the redundant classifiers. The experiments were conducted on the basis of four real-world data sets: Ionosphere data set, Thoracic Surgery data set, Blood Transfusion Service Center data set (btsc) and Statlog (Australian Credit Approval) data set from the machine-learning repository of University of California. The experimental results show that a given boosted classifier with our post optimization based on particle swarm optimization improves the classification accuracy for all used data. Also, the experiments show that the proposed algorithm outperforms other techniques with best generalization.
International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015
Abstract — The aim of this paper is to propose a hybrid classification algorithm based on particl... more Abstract — The aim of this paper is to propose a hybrid classification algorithm based on particle swarm optimization (PSO) to enhance the generalization performance of the Adaptive Boosting (AdaBoost) algorithm. AdaBoost enhances any given machine learning algorithm performance by producing some weak classifiers which requires more time and memory and may not give the best classification accuracy. For this purpose, We proposed PSO as a post optimization procedure for the resulted weak classifiers and removes the redundant classifiers. The experiments were conducted on the basis of four real-world data sets: Ionosphere data set, Thoracic Surgery data set, Blood Transfusion Service Center data set (btsc) and Statlog (Australian Credit Approval) data set from the machine-learning repository of University of California. The experimental results show that a given boosted classifier with our post optimization based on particle swarm optimization improves the classification accuracy for all used data. Also, the experiments show that the proposed algorithm outperforms other techniques with best generalization.
International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015
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IJCSIS Papers by Abeer Desuky
International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015
https://sites.google.com/site/ijcsis/
ISSN 1947-5500
Papers by Abeer Desuky
International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015
https://sites.google.com/site/ijcsis/
ISSN 1947-5500