The application of certainty factors to neural computing for rule discovery

Citation
Lm. Fu et Eh. Shortliffe, The application of certainty factors to neural computing for rule discovery, IEEE NEURAL, 11(3), 2000, pp. 647-657
Citations number
20
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON NEURAL NETWORKS
ISSN journal
10459227 → ACNP
Volume
11
Issue
3
Year of publication
2000
Pages
647 - 657
Database
ISI
SICI code
1045-9227(200005)11:3<647:TAOCFT>2.0.ZU;2-T
Abstract
Discovery of domain principles has been a major long-term goal for scientis ts. This paper presents a new system named DOMRUL for learning such princip les in the form of rules. A distinctive feature of the system is the integr ation of the certainty factor (CF) model and a neural network. These two el ements complement each other. The CF model offers the neural network better semantics and generalization advantage, and the neural network overcomes p ossible limitations such as inaccuracies and overcounting of evidence assoc iated with certainty factors. It is a major contribution of this paper to s how mathematically the quantizability nature of the CFNet since previously the quantizability of the CF model was demonstrated only empirically. The r ule discovery system can be applied to any domain without restriction on bo th the rule number and rule size, In a hypothetical domain, DOMRUL discover ed complex domain rules at a considerably higher accuracy than a commonly u sed rule-learning program C4.5 in both normal and noisy conditions, The sca lability in a large domain is also shown. On a real data set concerning pro moters prediction in molecular biology, DOMRUL learned rules with more comp lete semantics than C4.5.