Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

There has been a rapid growth in using Artificial Immune Systems for applications in data mining and computational intelligence recently. There are extensive computational aspects with the natural immune system. Several algorithms have been developed by exploiting these computational capabilities for a wide range of applications. Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. The results achieved by Artificial Immune Recognition Systems have shown the potential of Artificial Immune Systems to perform classification. Artificial Immune Recognition System is a relatively new classifier and has some advantages such as self regularity, parameter stability and data reduction capability. However, the Artificial Immune Recognition System uses a linear resource allocation method. This linearity increases the processing time of generating memory cells from antigens and causes an increase in the training time of the Artificial Immune Recognition System. Another problem with the Artificial Immune Recognition System is related to the resource competition phase which generates premature memory cells and decreases the classification accuracy of system. This thesis proposes new algorithms based on Artificial Immune Recognition System to address the mentioned weaknesses and improve the performance of the Artificial Immune Recognition System. Firstly, exponential-based resource allocation methods are utilized instead of the existing linear resource allocation method. Next, the Real World Tournament Selection method is adapted and incorporated into the resource competition of Artificial Immune Recognition System. The proposed algorithms have been tested on a variety of datasets from the UCI machine learning repository. The experimental results show that utilizing exponential-based resource allocation methods decreases the training time and increases the data reduction capability of Artificial Immune Recognition System. In addition, incorporating an adapted Real World Tournament Selection technique increases the accuracy of the Artificial Immune Recognition System up to 4%. The difference between the performances of the proposed algorithms and Artificial Immune Recognition System are significant in majority of cases

Improving the accuracy of AIRS by incorporating real world tournament selection in resource competition phase

Artificial Immune Recognition System (AIRS) is an immune inspired classifier that competes with famous classifiers. One of the most important components of AIRS is resource competition. The goal of resource competition is the development of the fittest individuals. Resource competition phase removes weakest individuals and selects strongest (seemly good) individuals. This type of selection has high selective pressure with a loss of diversity. It may generate premature memory cells and decrease the accuracy of classifier. In this study, the Real World Tournament Selection (RWTS) method is incorporated in resource competition phase of AIRS to prevent this issue and experiments are conducted to evaluate the accuracy of new algorithm (RWTSAIRS). The combination of cross validation and t test is used as evaluation method. Algorithms tested on benchmark datasets of the UCI machine learning repository show that RWTSAIRS obtained higher accuracy than AIRS in all cases and that the difference between accuracies of two algorithms was significant in majority of cases

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

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

A hybrid approach to traditional Malay music genre classification: combining feature selection and artificial immune recognition system

Music genre classification has a great important role in music information retrieval systems. In this study we propose hybrid approach for Traditional Malay Music (TMM) genre classification. The proposed approach consists of three stages: feature extraction, feature selection and classification with Artificial Immune Recognition System (AIRS). The new version of AIRS is used in this study. In Proposed algorithm, the resource allocation method of AIRS has been changed with a nonlinear method. Based on results of conducted experiments, the obtained classification accuracy of proposed system is 88.6 % using 10 fold cross validation. This accuracy is maximum accuracy among the classifiers used in this study