Expert Systems—Experiments with Rule Induction

Expert Querying and Redirection with Rule Responder

Rule Induction with Probabilisticrough Classifiers

We present an algorithm ProbRough for inducing decision rules from data. The algorithm combines all the positive aspects of rule induction systems with the exibility of the probabilistic representation of data and learned knowledge. It accepts imprecise and incomplete data of any mixed qualitative and quantitative type. The probabilistic structure of data is estimated from the learning set usin...

Rule-based Expert Systems

A general introduction to artificial intelligence methods of measurement signal processing is given in Article 128, Nature and Scope of AI Techniques, Volume 2. Problem solving is the process of finding a solution when the path leading to that solution is uncertain. Even though we are familiar with several problem-solving techniques, in the real world, sometimes many problems cannot be solved b...

Rule Induction with Extension Matrices

This paper presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0), based on the newly-developed extension matrix approach. By dividing the positive examples (PE) of a speciic class in a given example set into intersecting groups and adopting a set of strategies to nd a heuristic conjunctive formula in each group which covers all the group's positive example...

Preceding Rule Induction with Instance Reduction Methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of t...