Multi-knowledge extraction and application

Authors:
QingXiang Wu;David Bell
Affiliations:
Faculty of Informatics, University of Ulster at Magee, N.Ireland, UK;Department of Computer Science, Queens University, Belfast, UK
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

Citing 6
Cited 7

Computational methods for rough classification and discovery

Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough computational methods for information systems

Artificial Intelligence
Rough set methods and applications: new developments in knowledge discovery in information systems

Rough set methods and applications: new developments in knowledge discovery in information systems
Using Rough Sets with Heuristics for Feature Selection

Journal of Intelligent Information Systems
Rough Sets and Data Mining: Analysis of Imprecise Data

Rough Sets and Data Mining: Analysis of Imprecise Data
A Formalism for Relevance and Its Application in Feature Subset Selection

Machine Learning

A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches

IEEE Transactions on Knowledge and Data Engineering
A Rough Set and Fuzzy Neural Petri Net Based Method for Dynamic Knowledge Extraction, Representation and Inference in Cooperative Multiple Robot System

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Extracting Multi-knowledge from fMRI Data through Swarm-Based Rough Set Reduction

HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Multi-agent based multi-knowledge acquisition method for rough set

RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Improvement of decision accuracy using discretization of continuous attributes

FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
A novel discretizer for knowledge discovery approaches based on rough sets

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model

International Journal of Software Science and Computational Intelligence

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Abstract

Rough set theory provides approaches to the finding a reduct (informally, an identifying set of attributes) from a decision system or a training set. In this paper, an algorithm for finding multiple reducts is developed. The algorithm has been used to find the multi-reducts in data sets from UCI Machine Learning Repository. The experiments show that many databases in the real world have multiple reducts. Using the multi-reducts, multiknowledge is defined and an approach for extraction is presented. It is shown that a robot with multi-knowledge has the ability to identify a changing environment. Multiknowledge can be applied in many application areas in machine learning or data mining domain.