Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-1997

Abstract

This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. Besides that initializing networks with prior knowledge can improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can be refined and enhanced by the cascade ARTMAP learning algorithm. By preserving symbolic rule form during learning, the rules extracted from cascade ARTMAP can be compared directly with the originally inserted rules. Simulations on an animal identification problem indicate that a priori symbolic knowledge always improves system performance, especially with a small training set. Benchmark study on a DNA promoter recognition problem shows that with the added advantage of fast learning, cascade ARTMAP rule insertion and refinement algorithms produce performance superior to those of other machine learning systems and an alternative hybrid system known as knowledge-based artificial neural network (KBANN). Also, the rules extracted from cascade ARTMAP are more accurate and much cleaner than the NofM rules extracted from KBANN.

Keywords

ARTMAP, hybrid system, promotor recognition, rule extraction, rule insertion, rule refinement

Discipline

Databases and Information Systems | OS and Networks | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Neural Networks

Volume

8

Issue

2

First Page

237

Last Page

250

ISSN

1045-9227

Identifier

10.1109/72.557661

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/72.557661

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