Title :
Relationship between depth of decision trees and boosting performance
Author :
Guile, Geoffrey R. ; Wang, Wenkia
Author_Institution :
Sch. of Comput. Sci., Univ. of East Anglia, Norwich
Abstract :
We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high-dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic datasets. The results show that feature non-replacement enables decision trees deeper than simple stumps to be used, thereby allowing feature interaction to be taken into account.
Keywords :
DNA; biology computing; decision trees; learning (artificial intelligence); pattern classification; proteins; DNA microarray datasets; boosting performance; decision trees; ensemble learning algorithms; force classifiers; high-dimensional biological data; proteomic datasets; Boosting; Decision trees; Neural networks;
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.2008.4634111
