			Home About us Contact

Rough Set Theory (rough + set_theory)

Distribution by Scientific Domains

Information Science and Computing	80%

Selected Abstracts

Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction

COMPUTATIONAL INTELLIGENCE, Issue 4 2001
Xiaohua Hu
We present a method to learn maximal generalized decision rules from databases by integrating discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a pre-defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples in the database. In the second phase, a novel context-sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between classes and the attributes. Then rough set-based value reduction is further performed on the reduced table and all redundant condition values are dropped. Finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and a real market database demonstrate that our method can dramatically reduce the feature space and improve learning accuracy. [source]

An introduction of the condition class space with continuous value discretization and rough set theory

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 2 2006
Malcolm J. Beynon
The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space,the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes,reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST-related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173,191, 2006. [source]

Approaches to knowledge reductions in inconsistent systems

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 9 2003
Wen-Xiu Zhang
This article deals with approaches to knowledge reductions in inconsistent information systems (ISs). The main objective of this work was to introduce a new kind of knowledge reduction called a maximum distribution reduct, which preserves all maximum decision classes. This type of reduction eliminates the harsh requirements of the distribution reduct and overcomes the drawback of the possible reduct that the derived decision rules may be incompatible with the ones derived from the original system. Then, the relationships among the maximum distribution reduct, the distribution reduct, and the possible reduct were discussed. The judgement theorems and discernibility matrices associated with the three reductions were examined, from which we can obtain approaches to knowledge reductions in rough set theory (RST). © 2003 Wiley Periodicals, Inc. [source]

Designing an effective management system for enterprises: Concepts and verification

HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES, Issue 5 2008
Daria Mota
In this article lean and agile manufacturing are considered as separate organizational strategies of enterprise management. Therefore, a model of these strategies for business situations as well as assigning modern concepts and methods of management to these strategies and business segments has been proposed. This model, called LABDM (lean agile business development model), has been provisionally verified in small- and medium-sized enterprises from the gas engineering industry in the Wielkopolska province in Poland. In 17 enterprises of this industry the use of modern concepts and methods of management have been studied. With the help of the rough sets theory, a set of concepts and methods that are crucial for the effective enterprises has been identified. By comparing these concepts and methods to the LABDM, the model's rationality is proven. In conclusion, the LABDM can be used as a tool when considering a lean or agile strategy, and modern concepts and methods that are associated with these strategies. © 2008 Wiley Periodicals, Inc. [source]