On reduct construction algorithms

Authors:
Yiyu Yao;Yan Zhao;Jue Wang
Affiliations:
Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
Transactions on computational science II
Year:
2008

Citing 14
Cited 4

Feature Selection: Evaluation, Application, and Small Sample Performance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Rough set algorithms in classification problem

Rough set methods and applications
Various approaches to reasoning with frequency based decision reducts: a survey

Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction algorithms based on discernibility matrix: the ordered attributes method

Journal of Computer Science and Technology
A reduction algorithm meeting users' requirements

Journal of Computer Science and Technology
Using Rough Sets Theory and Database Operations to Construct a Good Ensemble of Classifiers for Data Mining Applications

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Rough Set-Aided System for Sorting WWW Bookmarks

WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Data mining tasks and methods: Rule discovery: rough set approaches for discovering rules and attribute dependencies

Handbook of data mining and knowledge discovery
Approaches to knowledge reduction based on variable precision rough set model

Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
A comparison of rough set methods and representative inductive learning algorithms

Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Information-theoretic measures of uncertainty for rough sets and rough relational databases

Information Sciences: an International Journal

Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts

Transactions on Computational Science V
Is it important which rough-set-based classifier extraction and voting criteria are applied together?

RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A novel feature selection method and its application

Journal of Intelligent Information Systems
Feature selection with test cost constraint

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper critically analyzes reduct construction methodsat two levels. At a high level, one can abstract commonalities from theexisting algorithms, and classify them into three basic groups based onthe underlying control structures. At a low level, by adopting differentheuristics or fitness functions for attribute selection, one is able to derivemost of the existing algorithms. The analysis brings new insights into theproblem of reduct construction, and provides guidelines for the design ofnew algorithms.