Artificial immune recognition system with nonlinear resource allocation method and application to traditional Malay music genre classification

• Main Authors: Hormozi, Shahram Golzari, C. Doraisamy, Shyamala, Sulaiman, Md. Nasir, Udzir, Nur Izura, Mohd Norowi, Noris
• Format: Conference or Workshop Item
• Language: English
• Published: Springer 2008
• Online Access: http://psasir.upm.edu.my/id/eprint/60429/1/Artificial%20immune%20recognition%20system%20with%20nonlinear%20resource%20allocation%20method%20and%20application%20to%20traditional%20Malay%20music%20genre%20classification.pdf; http://psasir.upm.edu.my/id/eprint/60429/

Artificial Immune Recognition System (AIRS) has shown an effective performance on several machine learning problems. In this study, the resource allocation method of AIRS was changed with a nonlinear method. This new algorithm, AIRS with nonlinear resource allocation method, was used as a classifier in Traditional Malay Music (TMM) genre classification. Music genre classification has a great important role in music information retrieval systems nowadays. The proposed system consists of three stages: feature extraction, feature selection and finally using proposed algorithm as a classifier. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation for TMM genre classification. The results also show that AIRS with nonlinear allocation method obtains maximum classification accuracy for TMM genre classification.