Principal feature classification

Author

Li, Qi ; Tufts, Donald W.

Author_Institution

Dept. of Electr. & Comput. Eng., Rhode Island Univ., Kingston, RI, USA

Volume

8

Issue

1

fYear

1997

fDate

1/1/1997 12:00:00 AM

Firstpage

155

Lastpage

160

Abstract

The concept, structures, and algorithms of principal feature classification (PFC) are presented in this paper. PFC is intended to solve complex classification problems with large data sets. A PFC network is designed by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advantages of statistical pattern recognition, decision trees, and artificial neural networks (ANNs) and provides fast learning with good performance and a simple network structure. For the real-world applications of this paper, PFC provides better performance than conventional statistical pattern recognition, avoids the long training times of backpropagation and other gradient-descent algorithms for ANNs, and provides a low-complexity structure for realization

Keywords

neural nets; pattern classification; statistical analysis; trees (mathematics); PFC; artificial neural networks; complex classification problems; decision trees; large data sets; low-complexity structure; principal feature classification; realization; statistical pattern recognition; Artificial neural networks; Backpropagation algorithms; Classification tree analysis; Decision trees; Feature extraction; Neural networks; Pattern recognition; Signal analysis; Signal processing algorithms; Training data;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.554200

Filename

554200