The Experts below are selected from a list of 30936 Experts worldwide ranked by ideXlab platform
Jiye Liang - One of the best experts on this subject based on the ideXlab platform.
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Accelerating incremental attribute reduction algorithm by compacting a Decision Table
International Journal of Machine Learning and Cybernetics, 2019Co-Authors: Wei Wei, Jiye Liang, Peng Song, Xiaoying WuAbstract:The evolution of object sets over time is ubiquitous in dynamic data. To acquire reducts for this type of data, researchers have proposed many incremental attribute reduction algorithms based on discernibility matrices. Although all reducts of an updated Decision Table can be obtained using these algorithms, their high computation time is a critical issue. To address this issue, we first construct three new types of discernibility matrices by compacting a Decision Table to eliminate redundant entries in the discernibility matrices of the original Decision Table. We then demonstrate that the set of reducts obtained from the compacted Decision Table are the same as those acquired from the original Decision Table. Extensive experiments have demonstrated that an incremental attribute reduction algorithm based on a compacted Decision Table can significantly accelerate attribute reduction for dynamic data with changing object sets while the acquired reducts are identical to those obtained using existing algorithms.
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a group incremental approach to feature selection applying rough set technique
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua QianAbstract:Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a Decision Table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a Decision Table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
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evaluation of the Decision performance of the Decision rule set from an ordered Decision Table
Knowledge Based Systems, 2012Co-Authors: Yuhua Qian, Peng Song, Jiye Liang, Chuangyin Dang, Wei WeiAbstract:An ordered Decision Table is one of the most effective frameworks for the intelligent Decision-making systems. As two classical measures, approximation accuracy and quality of approximation can be extended for evaluating the Decision performance of an ordered Decision Table. However, from the viewpoint of evaluating the Decision performance of a set of Decision rules, these two measures are still not able to well measure the entire certainty and consistency of an ordered Decision rule set. To overcome this deficiency, we first present three new measures for evaluating the Decision performance of a Decision-rule set extracted from an ordered Decision Table, and then analyze how each of these new measures depends on the condition granulation and the Decision granulation of an ordered Decision Table. Applications and experimental analysis of five types of ordered Decision Tables show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from each of these five types of Decision Tables and the results are much better than those of the two extended measures.
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a new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences, 2009Co-Authors: Jiye Liang, Junhong Wang, Yuhua QiaAbstract:In rough set theory, accuracy and roughness are used to characterize uncertainty of a set and approximation accuracy is employed to depict accuracy of a rough classification. Although these measures are effective, they have some limitations when the lower/upper approximation of a set under one knowledge is equal to that under another knowledge. To overcome these limitations, we address in this paper the issues of uncertainty of a set in an information system and approximation accuracy of a rough classification in a Decision Table. An axiomatic definition of knowledge granulation for an information system is given, under which these three measures are modified. Theoretical studies and experimental results show that the modified measures are effective and suiTable for evaluating the roughness and accuracy of a set in an information system and the approximation accuracy of a rough classification in a Decision Table, respectively, and have a much simpler and more comprehensive form than the existing ones.
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on the evaluation of the Decision performance of an incomplete Decision Table
Data and Knowledge Engineering, 2008Co-Authors: Yuhua Qian, Jiye Liang, Chuangyin Dang, Haiyun ZhangAbstract:As two classical measures, approximation accuracy and consistency degree can be extended for evaluating the Decision performance of an incomplete Decision Table. However, when the values of these two measures are equal to zero, they cannot give elaborate depictions of the certainty and consistency of an incomplete Decision Table. To overcome this shortcoming, we first classify incomplete Decision Tables into three types according to their consistency and introduce four new measures for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table. We then analyze how each of these four measures depends on the condition granulation and Decision granulation of each of the three types of incomplete Decision Tables. Experimental analyses on three practical data sets show that the four new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table and are much better than the two extended measures.
Yuhua Qian - One of the best experts on this subject based on the ideXlab platform.
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a group incremental approach to feature selection applying rough set technique
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua QianAbstract:Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a Decision Table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a Decision Table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
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evaluation of the Decision performance of the Decision rule set from an ordered Decision Table
Knowledge Based Systems, 2012Co-Authors: Yuhua Qian, Peng Song, Jiye Liang, Chuangyin Dang, Wei WeiAbstract:An ordered Decision Table is one of the most effective frameworks for the intelligent Decision-making systems. As two classical measures, approximation accuracy and quality of approximation can be extended for evaluating the Decision performance of an ordered Decision Table. However, from the viewpoint of evaluating the Decision performance of a set of Decision rules, these two measures are still not able to well measure the entire certainty and consistency of an ordered Decision rule set. To overcome this deficiency, we first present three new measures for evaluating the Decision performance of a Decision-rule set extracted from an ordered Decision Table, and then analyze how each of these new measures depends on the condition granulation and the Decision granulation of an ordered Decision Table. Applications and experimental analysis of five types of ordered Decision Tables show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from each of these five types of Decision Tables and the results are much better than those of the two extended measures.
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on the evaluation of the Decision performance of an incomplete Decision Table
Data and Knowledge Engineering, 2008Co-Authors: Yuhua Qian, Jiye Liang, Chuangyin Dang, Haiyun ZhangAbstract:As two classical measures, approximation accuracy and consistency degree can be extended for evaluating the Decision performance of an incomplete Decision Table. However, when the values of these two measures are equal to zero, they cannot give elaborate depictions of the certainty and consistency of an incomplete Decision Table. To overcome this shortcoming, we first classify incomplete Decision Tables into three types according to their consistency and introduce four new measures for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table. We then analyze how each of these four measures depends on the condition granulation and Decision granulation of each of the three types of incomplete Decision Tables. Experimental analyses on three practical data sets show that the four new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table and are much better than the two extended measures.
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measures for evaluating the Decision performance of a Decision Table in rough set theory
Information Sciences, 2008Co-Authors: Yuhua Qian, Jiye Liang, Haiyun Zhang, Chuangyin DangAbstract:As two classical measures, approximation accuracy and consistency degree can be employed to evaluate the Decision performance of a Decision Table. However, these two measures cannot give elaborate depictions of the certainty and consistency of a Decision Table when their values are equal to zero. To overcome this shortcoming, we first classify Decision Tables in rough set theory into three types according to their consistency and introduce three new measures for evaluating the Decision performance of a Decision-rule set extracted from a Decision Table. We then analyze how each of these three measures depends on the condition granulation and Decision granulation of each of the three types of Decision Tables. Experimental analyses on three practical data sets show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set and are much better than the two classical measures.
Chuangyin Dang - One of the best experts on this subject based on the ideXlab platform.
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a group incremental approach to feature selection applying rough set technique
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua QianAbstract:Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a Decision Table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a Decision Table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
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evaluation of the Decision performance of the Decision rule set from an ordered Decision Table
Knowledge Based Systems, 2012Co-Authors: Yuhua Qian, Peng Song, Jiye Liang, Chuangyin Dang, Wei WeiAbstract:An ordered Decision Table is one of the most effective frameworks for the intelligent Decision-making systems. As two classical measures, approximation accuracy and quality of approximation can be extended for evaluating the Decision performance of an ordered Decision Table. However, from the viewpoint of evaluating the Decision performance of a set of Decision rules, these two measures are still not able to well measure the entire certainty and consistency of an ordered Decision rule set. To overcome this deficiency, we first present three new measures for evaluating the Decision performance of a Decision-rule set extracted from an ordered Decision Table, and then analyze how each of these new measures depends on the condition granulation and the Decision granulation of an ordered Decision Table. Applications and experimental analysis of five types of ordered Decision Tables show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from each of these five types of Decision Tables and the results are much better than those of the two extended measures.
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on the evaluation of the Decision performance of an incomplete Decision Table
Data and Knowledge Engineering, 2008Co-Authors: Yuhua Qian, Jiye Liang, Chuangyin Dang, Haiyun ZhangAbstract:As two classical measures, approximation accuracy and consistency degree can be extended for evaluating the Decision performance of an incomplete Decision Table. However, when the values of these two measures are equal to zero, they cannot give elaborate depictions of the certainty and consistency of an incomplete Decision Table. To overcome this shortcoming, we first classify incomplete Decision Tables into three types according to their consistency and introduce four new measures for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table. We then analyze how each of these four measures depends on the condition granulation and Decision granulation of each of the three types of incomplete Decision Tables. Experimental analyses on three practical data sets show that the four new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from an incomplete Decision Table and are much better than the two extended measures.
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measures for evaluating the Decision performance of a Decision Table in rough set theory
Information Sciences, 2008Co-Authors: Yuhua Qian, Jiye Liang, Haiyun Zhang, Chuangyin DangAbstract:As two classical measures, approximation accuracy and consistency degree can be employed to evaluate the Decision performance of a Decision Table. However, these two measures cannot give elaborate depictions of the certainty and consistency of a Decision Table when their values are equal to zero. To overcome this shortcoming, we first classify Decision Tables in rough set theory into three types according to their consistency and introduce three new measures for evaluating the Decision performance of a Decision-rule set extracted from a Decision Table. We then analyze how each of these three measures depends on the condition granulation and Decision granulation of each of the three types of Decision Tables. Experimental analyses on three practical data sets show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set and are much better than the two classical measures.
Zhou Ruiqiong - One of the best experts on this subject based on the ideXlab platform.
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attribute reduction algorithm of continuous domain Decision Table based on fuzzy set
Computer Engineering, 2010Co-Authors: Zhou RuiqiongAbstract:Combining fuzzy set with rough set,attribute reduction algorithm of continuous domain Decision Table is studied.Continuous attribute values are transformed into fuzzy values with triangular membership function.Similarity degree of two fuzzy objects and similarity class of each fuzzy object are defined.Characteristic vector of continuous attribute which is made up of similarity class of each fuzzy object is provided.Digital characteristic vector of continuous attribute is presented and similar matrix of continuous attributes is proposed.A new attribute reduction algorithm is provided.Also,the algorithm is verified through an illustrative example.
Wei Wei - One of the best experts on this subject based on the ideXlab platform.
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Accelerating incremental attribute reduction algorithm by compacting a Decision Table
International Journal of Machine Learning and Cybernetics, 2019Co-Authors: Wei Wei, Jiye Liang, Peng Song, Xiaoying WuAbstract:The evolution of object sets over time is ubiquitous in dynamic data. To acquire reducts for this type of data, researchers have proposed many incremental attribute reduction algorithms based on discernibility matrices. Although all reducts of an updated Decision Table can be obtained using these algorithms, their high computation time is a critical issue. To address this issue, we first construct three new types of discernibility matrices by compacting a Decision Table to eliminate redundant entries in the discernibility matrices of the original Decision Table. We then demonstrate that the set of reducts obtained from the compacted Decision Table are the same as those acquired from the original Decision Table. Extensive experiments have demonstrated that an incremental attribute reduction algorithm based on a compacted Decision Table can significantly accelerate attribute reduction for dynamic data with changing object sets while the acquired reducts are identical to those obtained using existing algorithms.
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evaluation of the Decision performance of the Decision rule set from an ordered Decision Table
Knowledge Based Systems, 2012Co-Authors: Yuhua Qian, Peng Song, Jiye Liang, Chuangyin Dang, Wei WeiAbstract:An ordered Decision Table is one of the most effective frameworks for the intelligent Decision-making systems. As two classical measures, approximation accuracy and quality of approximation can be extended for evaluating the Decision performance of an ordered Decision Table. However, from the viewpoint of evaluating the Decision performance of a set of Decision rules, these two measures are still not able to well measure the entire certainty and consistency of an ordered Decision rule set. To overcome this deficiency, we first present three new measures for evaluating the Decision performance of a Decision-rule set extracted from an ordered Decision Table, and then analyze how each of these new measures depends on the condition granulation and the Decision granulation of an ordered Decision Table. Applications and experimental analysis of five types of ordered Decision Tables show that the three new measures appear to be well suited for evaluating the Decision performance of a Decision-rule set extracted from each of these five types of Decision Tables and the results are much better than those of the two extended measures.
