Dimensional reduction effects of feature vectors by coefficients of determination

Authors:
Jong-Wan Kim;Byung-Kon Hwang;Sin-Jae Kang;Hee-Jae Kim;Young-Cheol Oh
Affiliations:
School of Computer and Information Technology, Daegu University, South Korea;School of Computer and Information Technology, Daegu University, South Korea;School of Computer and Information Technology, Daegu University, South Korea;School of Computer and Information Technology, Daegu University, South Korea;School of Computer and Information Technology, Daegu University, South Korea
Venue:
CIS'04 Proceedings of the First international conference on Computational and Information Science
Year:
2004

Citing 4
Cited 0

Training algorithms for linear text classifiers

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Self-Organizing Maps

Self-Organizing Maps
Dimensionality Reduction through Sub-space Mapping for Nearest Neighbor Algorithms

ECML '00 Proceedings of the 11th European Conference on Machine Learning
Extraction of user preferences from a few positive documents

AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method to reduce features less contributing to the classification of user preferred news groups among several news groups by the use of fuzzy inference and coefficient of determination. To this end, we extract a number of representative keywords from example documents through fuzzy inference. From the observation of training patterns, we found that lots of keywords in training patterns are empty. Thus, a new method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is proposed in this paper. Experimental results show that the proposed method is superior to the method using lots of input attributes in terms of within-cluster variance and its standard deviation.