Knowledge discovery by inductive neural networks

Author

Fu, LiMin

Author_Institution

Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA

Volume

11

Issue

6

fYear

1999

Firstpage

992

Lastpage

998

Abstract

A new neural network model for inducing symbolic knowledge from empirical data is presented. This model capitalizes on the fact that the certainty factor-based activation function can improve the network generalization performance from a limited amount of training data. The formal properties of the procedure for extracting symbolic knowledge from such a trained neural network are investigated. In the domain of molecular genetics, a case study demonstrated that the described learning system effectively discovered the prior domain knowledge with some degree of refinement. Also, in cross-validation experiments, the system outperformed C4.5, a commonly used rule learning system

Keywords

data mining; generalisation (artificial intelligence); learning by example; learning systems; neural nets; uncertainty handling; C4.5; case study; certainty factor-based activation function; inductive neural networks; knowledge discovery; molecular genetics; network generalization performance; rule learning system; symbolic knowledge; training data; Computational complexity; Data mining; Genetics; Humans; Learning systems; Machine learning; Neural networks; Propulsion; Senior members; Training data;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.824623

Filename

824623