The Experts below are selected from a list of 178812 Experts worldwide ranked by ideXlab platform
Chang Feng - One of the best experts on this subject based on the ideXlab platform.
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A Value Reduction Algorithm Based on Rough Set Theory
Computer Engineering, 2003Co-Authors: Chang FengAbstract:This paper discusses the value Reduction Algorithm in rough set theory. It improves on the value Reduction Algorithm based on undistinguishable relation among decisions. So it can deal with every kind of instances in information system.
Zehua Chen - One of the best experts on this subject based on the ideXlab platform.
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A general Reduction Algorithm for relation decision systems and its applications
Knowledge-Based Systems, 2017Co-Authors: Guilong Liu, Zheng Hua, Zehua ChenAbstract:Give a general attribute Reduction Algorithm for relation decision systems.The Algorithm unifies earlier positive region attribute Reduction ones.Derive an Algorithm for complete, incomplete and numerical decision tables.The Reduction of covering decision systems is a special case of our Algorithm. This paper studies the attribute Reduction problem for general relation decision systems. We propose a new discernibility matrix to solve this problem. Combining the discernibility matrix and a recently proposed fast Algorithm, we propose a simple and unified attribute Reduction Algorithm for relation decision systems that is not contingent on the consistency of relation decision systems. We derive the Reduction Algorithm for the special cases of complete, incomplete, and numerical decision tables. As an application, we transform the attribute Reduction of relation decision systems into one for covering decision systems. This gives a convenient and effective Reduction Algorithm for covering decision systems. The Reduction results obtained using University of California Irvine data sets show that the proposed Algorithm is simple and efficient. Moreover, the proposed Algorithm enables the results of classical attribute Reduction approaches to be reinterpreted, giving them far greater unification and generality.
Edward Au - One of the best experts on this subject based on the ideXlab platform.
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ISIT - A modified state Reduction Algorithm for computing weight enumerators for convolutional codes
Proceedings. International Symposium on Information Theory 2005. ISIT 2005., 2005Co-Authors: Edward AuAbstract:The weight enumerator of a convolutional code is an important function that characterizes the codeword distance distribution and allows the error probability bounds of the code to be conveniently computed. An efficient state Reduction Algorithm to compute weight enumerators by iteratively modifying the symbolic adjacency matrix associated with the code was recently introduced. In this paper, we propose a dynamic elimination ordering technique that exploits the state diagram structure of the convolutional encoder for improving the efficiency of the state Reduction Algorithm
Zheng Hua - One of the best experts on this subject based on the ideXlab platform.
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ICMLC - Lower Assignment Reduction Algorithm for Relation Decision Systems
2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018Co-Authors: Zheng HuaAbstract:Attribute Reduction is an important and meaningful research in the field of rough set theory. Many Reduction Algorithms have been proposed based on different criteria. We now give an attribute Reduction Algorithm based on new criterion called the lower approximation assignment Reductions, which enriches and improves the concept of assignment Reductions in this paper. In addition, all Reduction sets could be obtained by using our Algorithms, while heuristic Algorithms only return one Reduction set. Also, we extend the lower assignment Reduction Algorithm for ordered decision systems and decision tables.
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A general Reduction Algorithm for relation decision systems and its applications
Knowledge-Based Systems, 2017Co-Authors: Guilong Liu, Zheng Hua, Zehua ChenAbstract:Give a general attribute Reduction Algorithm for relation decision systems.The Algorithm unifies earlier positive region attribute Reduction ones.Derive an Algorithm for complete, incomplete and numerical decision tables.The Reduction of covering decision systems is a special case of our Algorithm. This paper studies the attribute Reduction problem for general relation decision systems. We propose a new discernibility matrix to solve this problem. Combining the discernibility matrix and a recently proposed fast Algorithm, we propose a simple and unified attribute Reduction Algorithm for relation decision systems that is not contingent on the consistency of relation decision systems. We derive the Reduction Algorithm for the special cases of complete, incomplete, and numerical decision tables. As an application, we transform the attribute Reduction of relation decision systems into one for covering decision systems. This gives a convenient and effective Reduction Algorithm for covering decision systems. The Reduction results obtained using University of California Irvine data sets show that the proposed Algorithm is simple and efficient. Moreover, the proposed Algorithm enables the results of classical attribute Reduction approaches to be reinterpreted, giving them far greater unification and generality.
Vipin Kumar - One of the best experts on this subject based on the ideXlab platform.
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idr qr an incremental dimension Reduction Algorithm via qr decomposition
IEEE Transactions on Knowledge and Data Engineering, 2005Co-Authors: Hui Xiong, Haesun Park, Ravi Janardan, Vipin KumarAbstract:Dimension Reduction is a critical data preprocessing step for many database and data mining applications, such as efficient storage and retrieval of high-dimensional data. In the literature, a well-known dimension Reduction Algorithm is linear discriminant analysis (LDA). The common aspect of previously proposed LDA-based Algorithms is the use of singular value decomposition (SVD). Due to the difficulty of designing an incremental solution for the eigenvalue problem on the product of scatter matrices in LDA, there has been little work on designing incremental LDA Algorithms that can efficiently incorporate new data items as they become available. In this paper, we propose an LDA-based incremental dimension Reduction Algorithm, called IDR/QR, which applies QR decomposition rather than SVD. Unlike other LDA-based Algorithms, this Algorithm does not require the whole data matrix in main memory. This is desirable for large data sets. More importantly, with the insertion of new data items, the IDR/QR Algorithm can constrain the computational cost by applying efficient QR-updating techniques. Finally, we evaluate the effectiveness of the IDR/QR Algorithm in terms of classification error rate on the reduced dimensional space. Our experiments on several real-world data sets reveal that the classification error rate achieved by the IDR/QR Algorithm is very close to the best possible one achieved by other LDA-based Algorithms. However, the IDR/QR Algorithm has much less computational cost, especially when new data items are inserted dynamically.
