Improving rough classifiers using concept ontology

Authors:
Nguyen Sinh Hoa;Nguyen Hung Son
Affiliations:
Polish-Japanese Institute of Information Technology, Warsaw, Poland;Institute of Mathematics, Warsaw University, Warsaw, Poland
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 8
Cited 4

A new version of the rule induction system LERS

Fundamenta Informaticae
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer

Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Rough-Neuro-Computing: Techniques for Computing with Words

Rough-Neuro-Computing: Techniques for Computing with Words
Covering with Reducts - A Fast Algorithm for Rule Generation

RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSES and RSESlib - A Collection of Tools for Rough Set Computations

RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Handbook of data mining and knowledge discovery

Handbook of data mining and knowledge discovery
A view on rough set concept approximations

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

Hierarchical Classifiers for Complex Spatio-temporal Concepts

Transactions on Rough Sets IX
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach

Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Satisfiability judgement under incomplete information

Transactions on Rough Sets XI
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach

Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a method of classifier synthesis based on rough set theory and hierarchical learning idea. The improvement of the generated classifiers is achieved by using concept ontology as a domain knowledge. We examine the effectiveness of the proposed approach by comparing it with standard learning approaches with respect to different criteria. Our experiments are performed on benchmark data set as well as on artificial data sets generated by a road traffic simulator.