Essentials of artificial intelligence
Essentials of artificial intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A distributed evolutionary classifier for knowledge discovery in data mining
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
Computer Methods and Programs in Biomedicine
Improving support vector machine using a stochastic local search for classification in datamining
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Artificial Intelligence in Medicine
An evolutionary-based fuzzy resource assignment strategy for elastic traffic
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Real life data sets are often interspersed with noise, making the subsequent data mining process difficult. The task of the classifier could be simplified by eliminating attributes that are deemed to be redundant for classification, as the retention of only pertinent attributes would reduce the size of the dataset and subsequently allow more comprehensible analysis of the extracted patterns or rules. In this article, a new hybrid approach comprising of two conventional machine learning algorithms has been proposed to carry out attribute selection. Genetic algorithms (GAs) and support vector machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set by applying the principles of an evolutionary process. The SVM then classifies the patterns in the reduced datasets, corresponding to the attribute subsets represented by the GA chromosomes. The proposed GA-SVM hybrid is subsequently validated using datasets obtained from the UCI machine learning repository. Simulation results demonstrate that the GA-SVM hybrid produces good classification accuracy and a higher level of consistency that is comparable to other established algorithms. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This injects greater diversity and increases the overall fitness of the population. Similarly, the improved mechanism is also validated on the same data sets used in the first stage. The results justify the improvements in the classification accuracy and demonstrate its potential to be a good classifier for future data mining purposes.