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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

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Field Value
 
Title A Feature Selection process Optimization in multi-class Miner for Stream Data Classification
 
Creator Rai, Manish
pandit, Rekha
Richhariya, Vineet
 
Subject Computer Science; Information Technology
Stream Data classification; MCM; AGA and MGM-GA
 
Description 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.
 
Publisher CIRWORLD
 
Date 2004-12-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://cirworld.com/index.php/ijct/article/view/2939ijct
 
Source INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY; Vol 3 No 3a; 359-364
2277-3061
 
Language eng
 
Relation http://cirworld.com/index.php/ijct/article/view/2939ijct/2861
 
Rights Copyright (c) 2016 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
http://creativecommons.org/licenses/by/4.0
 

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