Title :
Eclectic method for feature reduction using Self-Organizing Maps
Author :
DeLooze, Lori L.
Author_Institution :
Comput. Sci. Dept., Naval Acad., Annapolis, MD
Abstract :
This paper presents an eclectic method for extracting simple classification rules using a combination of a genetic algorithm, a self-organizing map and the ID3 decision tree algorithm. After outlining the method for extracting rules, we assess them for effectiveness, complexity and precision and compare them with similar methods which use support vector machines. While it is no surprise that the method proposed reduced the complexity of classification, it was surprising that the simple rules extracted from the SOMs were both more effective and more precise than the SOM from which they were extracted.
Keywords :
genetic algorithms; self-organising feature maps; support vector machines; ID3 decision tree algorithm; classification rules extraction; eclectic method; feature reduction; genetic algorithm; self-organizing maps; support vector machines; Classification tree analysis; Decision trees; Feature extraction; Genetic algorithms; Genetic mutations; Pattern classification; Self organizing feature maps; Support vector machine classification; Support vector machines; Testing;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634082
