C4.5: programs for machine learning
C4.5: programs for machine learning
Computation of reducts of composed information systems
Fundamenta Informaticae - Special issue: rough sets
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Decision algorithms: a survey of rough set-theoretic methods
Fundamenta Informaticae - Special issue: intelligent information systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
Information Sciences: an International Journal
A new uncertainty measure of rough sets
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A rough set approach to feature selection based on power set tree
Knowledge-Based Systems
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
Reducts in incomplete decision tables
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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One of the main notions in the Rough Sets Theory (RST) is that of a reduct. According to its classic definition, the reduct is a minimal subset of the attributes that retains some important properties of the whole set of attributes. The idea of the reduct proved to be interesting enough to inspire a great deal of research and resulted in introducing various reduct-related ideas and notions. First of all, depending on the character of the attributes involved in the analysis, so called absolute and relative reducts can be defined. The more interesting of these, relative reducts, are minimal subsets of attributes that retain discernibility between objects belonging to different classes. This paper focuses on the topological aspects of such reducts, identifying some of their limitations and introducing alternative definitions that do not suffer from these limitations. The modified subsets of attributes, referred to as constructs, are intended to assist the subsequent inductive process of data generalisation and knowledge acquisition, which, in the context of RST, usually takes the form of decision rule generation. Usefulness of both reducts and constructs in this role is examined and evaluated in a massive computational experiment, which was carried out for a collection of real-life data sets.