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Audio Classification based on Association and Hybrid Optimization Technique
| 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
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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