Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.
|Title of host publication
|Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
|Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
|Number of pages
|Published - 2003
|14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan
Duration: 2003 Oct 28 → 2003 Oct 31
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
|03/10/28 → 03/10/31
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- General Computer Science