Asif Iqbal

Work place: PIRO Technologies PVT. LTD., New Delhi, India



Research Interests: Computer systems and computational processes, Artificial Intelligence, Computational Learning Theory, Data Structures and Algorithms


Asif Iqbal received his B.Tech degree in Electronics and Communication from Guru Gobind Singh Indraprastha University, New Delhi, India. Currently, he is CEO and Founder of PIRO Technologies PVT. LTD. His area of interest is application of artificial intelligence and machine learning in the different field of science and engineering. He has worked with many academic/industrial researchers and has more than 5 publications in reputed journals and conference

Author Articles
Threshold Controlled Binary Particle Swarm Optimization for High Dimensional Feature Selection

By Sonu Lal Gupta Anurag Singh Baghel Asif Iqbal

DOI:, Pub. Date: 8 Aug. 2018

Dimensionality reduction or the optimal selection of features is a challenging task due to large search space. Currently, many research has been performed in this domain to improve the accuracy as well as to minimize the computational complexity. Particle Swarm Optimization (PSO) based feature selection approach seems very promising and has been extensively used for this work. In this paper, a Threshold Controlled Binary Particle Swarm Optimization (TC-BPSO) along with Multi-Class Support Vector Machine (MC-SVM) is proposed and compared with Conventional Binary Particle Swarm Optimization (C-BPSO). TC-BPSO is used for the selection of features while MC-SVM is used to calculate the classification accuracy. 70% of the data is used to train the MC-SVM model while the test has been performed on rest 30% data to calculate the accuracy. Proposed approach is tested on ten different datasets having varying difficulties such as some datasets having large number of features while some have small, some have just two classes while some have many classes, some datasets having small number of instances while some have large number of instances and the results obtained on these datasets are compared with some of the existing methods. Experiments show that the obtained results are very promising and achieved the best accuracy in minimum possible features. Proposed approach outperforms C-BPSO in all contexts on most of the datasets and 3-4 times computationally faster. It also outperforms in all context when compared with the existing work and 5-8 times computationally faster.

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