Modified Multi-Class Miner using Particle of Swarm Optimization for Stream Data Classification
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10.5120/15589-4353 |
Archana Bopche and Parmalik Kumar. Article: Modified Multi-Class Miner using Particle of Swarm Optimization for Stream Data Classification. International Journal of Computer Applications 90(7):44-47, March 2014. Full text available. BibTeX
@article{key:article,
author = {Archana Bopche and Parmalik Kumar},
title = {Article: Modified Multi-Class Miner using Particle of Swarm Optimization for Stream Data Classification},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {90},
number = {7},
pages = {44-47},
month = {March},
note = {Full text available}
}
Abstract
Multi-class miner is well recognized method for stream data classification. For the process of multi-class miner evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of multi-class miner (MCM). For the improvement of multi-class miner particle of swarm optimization technique is used. Particle of swarm optimization controls the dynamic feature evaluation process and decreases the possibility of confusion in selection of class and increase the classification ratio of multi-class miner. Particle of swarm optimization work in two phases one used as dynamic population selection and another are used for optimization process of evolved new feature. For the performance evaluation modified MCM algorithm implemented in MATLAB. For the validation of modified multi-class miner (MMCM) used sample dataset from UCI machine learning repository . Our empirical evaluation shows that better result in compression of multi-class miner and also increases the classification ratio of stream data classification.
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