Automatic Musical Genre Classification and Artificial Immune Recognition System

Doraisamy, Shyamala; Golzari, Shahram

doi:10.1007/978-3-642-11674-2_17

Cited by 6 publications

References 11 publications

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“…It is progressing slowly and steadily as a new branch of computational intelligence and soft computing (de Castro and Timmis, 2002;de Castro and Timmis, 2003;Golzari et al, 2009). It has been used in several applications such as machine learning, pattern recognition, computer virus detection, anomaly detection, optimization and genre classification (de Castro and Timmis, 2002;de France et al, 2005;Igawa and Ohashi, 2007;Kim et al, 2007;Doraisamy and Golzari, 2010). One of AIS based algorithms is Artificial Immune Recognition System (AIRS).…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Immune Based Classifier

Golzari¹,

Doraisamy²,

Sulaiman³

et al. 2011

Journal of Computer Science

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Problem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation method is proposed to make AIRS more efficient. New algorithm, AIRS with proposed nonlinear method, is tested on benchmark datasets from UCI machine learning repository. Results: Based on the results of experiments, using proposed nonlinear resource allocation method decreases the training time and number of memory cells and doesn't reduce the accuracy of AIRS. Conclusion: The proposed classifier is an efficient and effective classifier.

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