A Feature Selection process Optimization in multi-class Miner for Stream Data Classification

Authors

  • Manish Rai LNCT BHOPAL RGPV UNIVERSITY BHOPAL MP
  • Rekha pandit Department of Computer Science and Engg, LNCT BHOPAL RGPV UNIVERSITY BHOPAL MP
  • Vineet Richhariya LNCT BHOPAL

DOI:

https://doi.org/10.24297/ijct.v3i3a.2939

Keywords:

Stream Data classification, MCM, AGA and MGM-GA

Abstract

Multi-class miner resolves the problem of feature evaluation, data drift and concept evaluation of stream data classification. The process of stream data classification in multi-class miner based on ensemble technique of clustering and classification on feature evaluation technique. The process of feature evaluation technique faced a problem of correct point selection of cluster centre for the process of data grouping. For the proper selection of features point we used optimization technique for feature selection process. The feature selection process based on advance genetic algorithm (AGA). The advance genetic algorithm poses a process of feature point for neighbour class detection for finding a correct point in classification. Our proposed algorithm tested on some well know data set provided by UCI machine learning repository. Our empirical evaluation result shows that better result in comparison of multi-class miner for stream data classification.

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Author Biographies

Manish Rai, LNCT BHOPAL RGPV UNIVERSITY BHOPAL MP

M. Tech

Rekha pandit, Department of Computer Science and Engg, LNCT BHOPAL RGPV UNIVERSITY BHOPAL MP

Professor

Vineet Richhariya, LNCT BHOPAL

HOD, DEPARTMENT OF CSE

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Published

2012-11-22

How to Cite

Rai, M., pandit, R., & Richhariya, V. (2012). A Feature Selection process Optimization in multi-class Miner for Stream Data Classification. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 3(3), 359-364. https://doi.org/10.24297/ijct.v3i3a.2939

Issue

Section

Research Articles