Efficient Pruning Methods for Separate-and-Conquer Rule Learning Systems.
Published 1 January 1993
William W. Cohen
Citations82
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper presents a solution in the form of new pruning techniques that dramatically improve the runtime of rule induction methods with no loss in accuracy: formal analysis shows an improvement in asymp-totic time complexity, and experiments show an order-of-magnitude speedup.
Abstract
Recent years have seen increased interest in systems that learn sets of rules. The goal of this paper is to study the degree to which "separate and conquer" rule learning induction methods scale up to large, real-world learning problems. In particular
Keywords
Computer Science