Audio Classification based on Association and Hybrid Optimization Technique

Abstract

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