The Experts below are selected from a list of 16905 Experts worldwide ranked by ideXlab platform
Lixiang Shen - One of the best experts on this subject based on the ideXlab platform.
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Fault diagnosis based on Rough Set Theory
Engineering Applications of Artificial Intelligence, 2003Co-Authors: Francis E. H. Tay, Lixiang ShenAbstract:Abstract In contingency management of a complex system, identification of error condition or faults diagnosis is a very important stage. It determines the methods and techniques to be applied in the following stages of contingency management. In this paper, Rough Set Theory as a new fault-diagnosing tool is used to identify the valve fault for a multi-cylinder diesel engine. This method overcomes the shortcoming of conventional methods where each method of fault diagnosis on diesel engine can only provide one corresponding fault category. By the analysis of the final reducts generated using Rough Set Theory, it is shown that this new method is effective for valve fault diagnosis and it is a new powerful tool that can be applied in contingency management.
Jiye Liang - One of the best experts on this subject based on the ideXlab platform.
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Information fusion in Rough Set Theory : An overview
Information Fusion, 2019Co-Authors: Wei Wei, Jiye LiangAbstract:Abstract Rough Set Theory is an efficient tool for dealing with inexact and uncertain information. Numerous studies have focused on Rough Set Theory and associated methodologies, and in recent decades, various models and algorithms have been proposed. To clarify the application of information fusion in Rough Set Theory, this paper presents an overview of existing information fusion approaches and methods for multi-source, multi-modality, multi-scale, and multi-view information systems from the perspective of objects, attributes, Rough approximations, attribute reduction, and decision making. We provide a survey of recent applications of these theories and methods in various fields, and identify some potential challenges that require further research.
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Distance: A more comprehensible perspective for measures in Rough Set Theory
Knowledge-Based Systems, 2012Co-Authors: Jiye Liang, Yuhua QianAbstract:Distance provides a comprehensible perspective for characterizing the difference between two objects in a metric space. There are many measures which have been proposed and applied for various targets in Rough Set Theory. In this study, thRough introducing Set distance and partition distance to Rough Set Theory, we investigate how to understand measures from Rough Set Theory in the viewpoint of distance, which are inclusion degree, accuracy measure, Rough measure, approximation quality, fuzziness measure, three decision evaluation criteria, information measure and information granularity. Moreover, a Rough Set framework based on the Set distance is also a very interesting perspective for understanding Rough Set approximation. From the view of distance, these results look forward to providing a more comprehensible perspective for measures in Rough Set Theory.
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positive approximation an accelerator for attribute reduction in Rough Set Theory
Artificial Intelligence, 2010Co-Authors: Yuhua Qian, Jiye Liang, Witold Pedrycz, Chuangyin DangAbstract:Feature selection is a challenging problem in areas such as pattern recognition, machine learning and data mining. Considering a consistency measure introduced in Rough Set Theory, the problem of feature selection, also called attribute reduction, aims to retain the discriminatory power of original features. Many heuristic attribute reduction algorithms have been proposed however, quite often, these methods are computationally time-consuming. To overcome this shortcoming, we introduce a theoretic framework based on Rough Set Theory, called positive approximation, which can be used to accelerate a heuristic process of attribute reduction. Based on the proposed accelerator, a general attribute reduction algorithm is designed. ThRough the use of the accelerator, several representative heuristic attribute reduction algorithms in Rough Set Theory have been enhanced. Note that each of the modified algorithms can choose the same attribute reduct as its original version, and hence possesses the same classification accuracy. Experiments show that these modified algorithms outperform their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data Sets.
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combination entropy and combination granulation in Rough Set Theory
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2008Co-Authors: Yuhua Qian, Jiye LiangAbstract:Based on the intuitionistic knowledge content nature of information gain, the concepts of combination entropy and combination granulation are introduced in Rough Set Theory. The conditional combination entropy and the mutual information are defined and their several useful properties are derived. Furthermore, the relationship between the combination entropy and the combination granulation is established, which can be expressed as CE(R) + CG(R) = 1. All properties of the above concepts are all special instances of those of the concepts in incomplete information systems. These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in a heuristic reduct algorithm in Rough Set Theory.
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THE INFORMATION ENTROPY, Rough ENTROPY AND KNOWLEDGE GRANULATION IN Rough Set Theory
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2004Co-Authors: Jiye Liang, Zhongzhi ShiAbstract:Rough Set Theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, Rough entropy and knowledge granulation in Rough Set Theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in Rough Set Theory.
Jose Manuel Benitez - One of the best experts on this subject based on the ideXlab platform.
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implementing algorithms of Rough Set Theory and fuzzy Rough Set Theory in the r package RoughSets
Information Sciences, 2014Co-Authors: Lala Septem Riza, Andrzej Janusz, Christoph Bergmeir, Chris Cornelis, Francisco Herrera, Dominik śle Zak, Jose Manuel BenitezAbstract:Abstract The package RoughSets , written mainly in the R language, provides implementations of methods from the Rough Set Theory (RST) and fuzzy Rough Set Theory (FRST) for data modeling and analysis. It considers not only fundamental concepts (e.g., indiscernibility relations, lower/upper approximations, etc.), but also their applications in many tasks: discretization, feature selection, instance selection, rule induction, and nearest neighbor-based classifiers. The package architecture and examples are presented in order to introduce it to researchers and practitioners. Researchers can build new models by defining custom functions as parameters, and practitioners are able to perform analysis and prediction of their data using available algorithms. Additionally, we provide a review and comparison of well-known software packages. Overall, our package should be considered as an alternative software library for analyzing data based on RST and FRST.
Francis E. H. Tay - One of the best experts on this subject based on the ideXlab platform.
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Fault diagnosis based on Rough Set Theory
Engineering Applications of Artificial Intelligence, 2003Co-Authors: Francis E. H. Tay, Lixiang ShenAbstract:Abstract In contingency management of a complex system, identification of error condition or faults diagnosis is a very important stage. It determines the methods and techniques to be applied in the following stages of contingency management. In this paper, Rough Set Theory as a new fault-diagnosing tool is used to identify the valve fault for a multi-cylinder diesel engine. This method overcomes the shortcoming of conventional methods where each method of fault diagnosis on diesel engine can only provide one corresponding fault category. By the analysis of the final reducts generated using Rough Set Theory, it is shown that this new method is effective for valve fault diagnosis and it is a new powerful tool that can be applied in contingency management.
Lala Septem Riza - One of the best experts on this subject based on the ideXlab platform.
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implementing algorithms of Rough Set Theory and fuzzy Rough Set Theory in the r package RoughSets
Information Sciences, 2014Co-Authors: Lala Septem Riza, Andrzej Janusz, Christoph Bergmeir, Chris Cornelis, Francisco Herrera, Dominik śle Zak, Jose Manuel BenitezAbstract:Abstract The package RoughSets , written mainly in the R language, provides implementations of methods from the Rough Set Theory (RST) and fuzzy Rough Set Theory (FRST) for data modeling and analysis. It considers not only fundamental concepts (e.g., indiscernibility relations, lower/upper approximations, etc.), but also their applications in many tasks: discretization, feature selection, instance selection, rule induction, and nearest neighbor-based classifiers. The package architecture and examples are presented in order to introduce it to researchers and practitioners. Researchers can build new models by defining custom functions as parameters, and practitioners are able to perform analysis and prediction of their data using available algorithms. Additionally, we provide a review and comparison of well-known software packages. Overall, our package should be considered as an alternative software library for analyzing data based on RST and FRST.
