Multi-Channel Parallel Adaptation Theory for Rule Discovery

TL;DR

This paper presents a new multi-channel parallel adaptation theory for rule discovery, introducing the CFRule model that converges to target rules and can explicitly encode rule sets, demonstrated in bioinformatics applications.

Contribution

The paper develops a novel multi-channel parallel adaptation theory and a corresponding CFRule model that achieves explicit rule encoding and convergence properties for rule learning.

Findings

CFRule can explicitly encode any rule set.

The model converges asynchronously to target rules.

Practical applications include DNA promoter recognition and hepatitis prognosis.

Abstract

In this paper, we introduce a new machine learning theory based on multi-channel parallel adaptation for rule discovery. This theory is distinguished from the familiar parallel-distributed adaptation theory of neural networks in terms of channel-based convergence to the target rules. We show how to realize this theory in a learning system named CFRule. CFRule is a parallel weight-based model, but it departs from traditional neural computing in that its internal knowledge is comprehensible. Furthermore, when the model converges upon training, each channel converges to a target rule. The model adaptation rule is derived by multi-level parallel weight optimization based on gradient descent. Since, however, gradient descent only guarantees local optimization, a multi-channel regression-based optimization strategy is developed to effectively deal with this problem. Formally, we prove that the CFRule model can explicitly and precisely encode any given rule set. Also, we prove a property related to asynchronous parallel convergence, which is a critical element of the multi-channel parallel adaptation theory for rule learning. Thanks to the quantizability nature of the CFRule model, rules can be extracted completely and soundly via a threshold-based mechanism. Finally, the practical application of the theory is demonstrated in DNA promoter recognition and hepatitis prognosis prediction.