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Reseach Article

Classification of Concept-Drifting Data Streams using Optimized Genetic Algorithm

by E. Padmalatha, C.R.K. Reddy, B. Padmaja Rani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 15
Year of Publication: 2015
Authors: E. Padmalatha, C.R.K. Reddy, B. Padmaja Rani
10.5120/ijca2015905842

E. Padmalatha, C.R.K. Reddy, B. Padmaja Rani . Classification of Concept-Drifting Data Streams using Optimized Genetic Algorithm. International Journal of Computer Applications. 125, 15 ( September 2015), 1-6. DOI=10.5120/ijca2015905842

@article{ 10.5120/ijca2015905842,
author = { E. Padmalatha, C.R.K. Reddy, B. Padmaja Rani },
title = { Classification of Concept-Drifting Data Streams using Optimized Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 15 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number15/22505-2015905842/ },
doi = { 10.5120/ijca2015905842 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:10.008482+05:30
%A E. Padmalatha
%A C.R.K. Reddy
%A B. Padmaja Rani
%T Classification of Concept-Drifting Data Streams using Optimized Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 15
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. In these applications, the main goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion.In many applications which are in non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e., the class or the target value to be predicted may change over time. This problem is referred to as Concept drift[8]. Evolutionary Computations like Genetic Algorithm is a strong rule based classification algorithm which is used for mining static small data sets and inefficient for large data streams. Evolutionary Algorithms are one of the population optimization techniques done by calculating fitness evaluation measures using gene reproduction, crossover, mutation and selection of the individual gene mechanisms. If the Genetic Algorithm can be made scalable and adaptable by reducing its I/O intensity, it will become an efficient and effective tool for mining large data sets like data streams.In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data Stream conceptdrift Genetic Algorithm optimization.