International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Attribute reduction is one of key issues in rough set theory, and positive region reduct is a classical type of reducts. However, a lot of reduction algorithms have more high time expenses when dealing with high-volume and high-dimensional data sets. To overcome this shortcoming, in this paper, a relative discernibility reduction method based on the simplified decision table of the original decision table is researched for obtaining positive region reduct. Moreover, to further improve performance of reduction algorithm, we develop an accelerator for attribute reduction, which reduces the radix sort times of the reduction process to raise algorithm efficiency. By the accelerator, two positive region reduction algorithms, i.e., FARA-RS and BARA-RS, based on the relative discernibility are designed. FARA-RS simultaneously reduce the size of the universe and the number of radix sort to achieve speedup and BARA-RS only reduce the number of radix sort to achieve acceleration. The experimental results show that the proposed reduction algorithms are effective and feasible for high dimensional and large data sets.

This work presents an overview of the text mining area, considering the most common techniques, and including proposals based on the application of fuzzy sets. Besides, some of the most frequent text mining applications are mentioned. We discuss the existing approaches, which we call text data mining, in relation to the recently proposed paradigm of text knowledge mining, and we conclude that both are different and complementary, in the sense that they are able to extract different knowledge pieces from text by using different reasoning mechanisms. Future challenges related to text knowledge mining are also briefly outlined.

Researchers usually extract domain experts only through analyzing network structure or partitioning users into several communities according to their label information. Combining structure and content to discovery domain experts is a new attempt. Motivated by that, this paper proposes a domain expert discovery method based on network structure and content semantics, called DEDSC, which can extract authority nodes in overlapping communities. To analyze the overall authority for each user in the social network, two definitions, structure authority value and content authority value, are proposed to evaluate the authority of users in different perspectives. Partitioning users into communities can make the results more accurate. Experimental results show that our proposed method can discover domain experts effectively. In addition, when we need to extract domain experts in a new test dataset, we do not need to re-train the data in the training dataset.

We develop a novel computation model of unbalanced linguistic variables on the basis of Archimedean copulas and corresponding co-copulas, which provides a new tool to aggregate unbalanced linguistic information. The properties of the proposed computational model are also studied. We present the concepts of weighted unbalanced Archimedean copula arithmetic aggregation operators and weighted unbalanced Archimedean copula geometric aggregation operators. The properties of these aggregation operators are further investigated. Finally, a group decision making of sensory evaluation is introduced to illustrate the feasibility and validity of our proposed computational model.

This paper develops some new decision making methods for multi-attribute decision making (MADM) problems, in which the attribute weights take the form of crisp numbers, and attribute values take the form of interval-valued intuitionistic fuzzy information. First, based on the Zhenyuan integral, an interval-valued intuitionistic fuzzy Zhenyuan averaging (IVIFZA) operator and an interval-valued intuitionistic fuzzy Zhenyuan geometric (IVIFZG) operator are introduced to facilitate aggregation of interval-valued intuitionistic fuzzy information. The proposed operators allow one to fully consider the importance of different combinations of attributes and, therefore, are highly suitable to handle problems involving inter-dependent or interactive attributes. We further proceed by exploring some desirable properties of the IVIFZA and IVIVZG operators. By employing the proposed operators, a MADM approach based on intervalvalued intuitionistic fuzzy information is proposed. Finally, an illustrative example is presented to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper, an order induced by implications on a bounded lattice under some more lenient conditions than the ones given former studies is defined and some of its properties are discussed. By giving an order based on uninorms on a bounded lattice, the relationships between such generated orders are investigated.

The option-pricing problem is an important topic in modern finance. In this paper, we propose a stock model with varying stock diffusion based on uncertainty theory. The European option pricing formulas are derived from the proposed uncertain stock model, and some mathematical properties of these formulas are investigated. Moreover, extended uncertain stock models are introduced and discussed. Finally, numerical examples are given to illustrate the proposed model.

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.