Earticle

초록

영어

In this paper we have proposed a modified PSO based classification model for multidimensional real dataset and we have studied the results of experiment by implementing particle swarm optimization in classification. Evaluation of the performance of PSO based classifier has been made by considering several variation of parameters of the standard particle swarm optimizer. Here we have used a multidimensional real cancer dataset for classification using PSO to study the behavior of PSO parameters and to observe the accuracy of classification of PSO based classifier in different iterations. Extensive simulation has been carried out using UCI data, on which classification is done using our proposed algorithm. We have also explored the possible influence of variants of PSO on accuracy of classification. The obtained results indicate that particle swarm optimization is an effective technique for classification and can be used successfully on more demanding problem domain.

목차

Abstract
 1. Introduction
 2. Training Phase from Real Data
 3. Result Analysis and Experimentation
 4. PSO Variants Verses Accuracy in Classification
 5. Conclusion
 6. Future Works
 References

키워드

저자

• Sarita Mahapatra [ Dept of Information Technology, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubanewar, Orissa, India ]
• Alok Kumar Jagadev [ Dept of Information Technology, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubanewar, Orissa, India ]
• Bighnaraj Naik [ Dept of Information Technology, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubanewar, Orissa, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605