A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation

TL;DR

This paper introduces a versatile algorithm that converts rule-based domain knowledge into artificial samples, enhancing various inductive learning methods by improving their performance across multiple scenarios.

Contribution

The proposed method generalizes the integration of rule-based knowledge into any inductive learning algorithm through virtual sample creation and data cleansing.

Findings

Enhanced classification accuracy over standard inductive methods

Effective incorporation of propositional rules into learning algorithms

Versatile approach applicable to multiple learning scenarios

Abstract

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning.