Title :
Quantitative feature evaluation using hybrid neural network and fuzzy logic approach
Author :
Jiang, Hao ; Feng, Xin
Author_Institution :
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
Abstract :
This paper presents a hybrid feature evaluation method using a competitive learning neural network and fuzzy logic for the analysis of high dimensional data. Not only can we give the quantitative information of the relative importance of features but the contributions of features to each data category can be observed during the analysis. The motivation of this study is to provide a method to discover the nature of data represented by multiple features by evaluating the importance of features representing data and the data best describing the information embedded by features.
Keywords :
feature extraction; fuzzy logic; learning systems; neural nets; unsupervised learning; data category; embedded information; fuzzy logic; high dimensional data; hybrid feature evaluation method; learning neural network; quantitative information; Artificial neural networks; Computer networks; Data analysis; Data engineering; Data mining; Feature extraction; Fuzzy logic; Neural networks; Neurons; Principal component analysis;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223383
