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Audio Classification based on Association and Hybrid Optimization Technique

by Gurdeep Singh, Shruti Aggarwal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 23
Year of Publication: 2015
Authors: Gurdeep Singh, Shruti Aggarwal
10.5120/21401-4412

Gurdeep Singh, Shruti Aggarwal . Audio Classification based on Association and Hybrid Optimization Technique. International Journal of Computer Applications. 120, 23 ( June 2015), 19-25. DOI=10.5120/21401-4412

@article{ 10.5120/21401-4412,
author = { Gurdeep Singh, Shruti Aggarwal },
title = { Audio Classification based on Association and Hybrid Optimization Technique },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 23 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number23/21401-4412/ },
doi = { 10.5120/21401-4412 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:59.322032+05:30
%A Gurdeep Singh
%A Shruti Aggarwal
%T Audio Classification based on Association and Hybrid Optimization Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 23
%P 19-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In pattern recognition areas and data mining, audio data classification is a most important topic. This paper describes a new classification method, where Optimal Classification Rule Extraction for multi-class Audio Data (O-cREAD). This classification method uses a new hybrid optimization approach for extracting optimal classification rules, and then these optimal rules are further used for classifying multi-class testing audio data to their respective classes with better accuracy. The optimal classification rule extraction is a two-step process. In the first step, frequent itemsets are generated by the hybrid apriori algorithm and generates classification rules using the association concept. Next, a new hybrid optimization approach is used for optimizing classification rules of classification method. The new hybrid optimization approach is based on Ant Colony Optimization (ACO) and Multi-Objective Genetic Algorithm (MOGA). The best feature of classification method (O-cREAD) is that size of classification rules can be dramatically reduced and produce more sophisticated or complicated rules to improve classification accuracy for classifying a real audio dataset into their respective classes.

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Index Terms

Computer Science
Information Sciences

Keywords

Audio Data Classification Rules Hybrid Apriori Algorithm (H-AA) Genetic Algorithm (GA) Ant colony optimization (ACO) Hybrid optimization approach.

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