A New Version of the Rule Induction System LERS
Fundamenta InformaticaePublished 1 July 1997
Jerzy W. Grzymala‐Busse
Citations462
SJR quartileQ3
SJR score0.29
SNIP0.62
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
A new version of the rule induction system LERS is described and compared with the old version and the new LERS system performance is fully comparable with performance of the other two systems.
Abstract
A new version of the rule induction system LERS is described and compared with the old version of LERS. Experiments were done for comparison of performance for both versions of LERS and the two other rule-induction systems: AQ15 and C4.5. The new LERS system performance is fully comparable with performance of the other two systems.
Keywords
Computer Science
Rough Sets: Theoretical Aspects of Reasoning about Data
8,416 Citations1991Zdzisław Pawlak
National Conference on Artificial IntelligenceThe multi-purpose incremental learning system AQ15 and its testing application to three medical domains
761 Citations1986Ryszard S. Michalski, Igor Mozetič +2 more
The demonstration that by applying the proposed method of cover truncation and analogical matching, called TRUNC, one may drastically decrease the complexity of the knowledge base without affecting its performance accuracy is demonstrated.
LERS-A System for Learning from Examples Based on Rough Sets
649 Citations1992Jerzy W. Grzymala‐Busse
The paper presents the system LERS for rule induction, which handles inconsistencies in the input data due to its usage of rough set theory principle and induces all rules, each in the minimal form, that can be induced from the inputData.
TechnometricsStatistical Analysis for Decision Making
198 Citations1978Shimshon Kinory, Morris Hamburg
Managing Uncertainty in Expert Systems
197 Citations1991Jerzy W. Grzymala‐Busse
The architecture of an Expert System, a guide to expert systems, and some of the techniques used to develop and evaluate these systems:.
Computational IntelligenceEXTRACTING LAWS FROM DECISION TABLES: A ROUGH SET APPROACH
149 Citations1995Andrzej Skowron
Two methods of searching for new classifiers (features) are described: searching fornew classifiers in a given set of logical formulas, and searching for some functions approximating near‐to‐functional relations.
Workshops in computingHandling Various Types of Uncertainty in the Rough Set Approach
52 Citations1994Roman Słowiński, Jerzy Stefanowski
The paper refers to problems of handling various types of uncertainty in the rough set approach to analysis of information systems, and considers uncertainty caused by: discretization of quantitative attributes, imprecise descriptors, unknown descriptor, or multiple descriptors.
Workshops in computingESEP: An Expert System for Environmental Protection
5 Citations1994Jerzy W. Grzymala‐Busse
An expert system called ESEP (Expert System for Environmental Protection) was developed to enhance facility compliance under Sections 311, 312, and 313 of the Emergency Planning and Community Right to Know Act.
