International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Peer-to-Peer (P2P) lending brings many benefits for both lenders and borrowers, as well as risk for them, especially for the lenders. To help the lenders select a reliable P2P lending platform with high return and low risk, a more comprehensive selection method is necessary. However, vast information with fluctuant, fuzzy and subjective data on the P2P lending platform make it more difficult for lenders to make the right choice. This paper intends to propose a selection method by using intuitionistic fuzzy soft set to organize the data, and using D-S theory of evidence to integrate the intuitionistic fuzzy values. Besides, the paper analyses the critical factors affecting the lenders’ decision, and presents some indicators for evaluating. Finally, an empirical experiment was given by choosing several P2P lending platforms with real data, and the results are compared with the intuitionistic fuzzy weighted average and Development Index from Wangdaizhijia, that verify the validity and superiority of the proposed method.

Social business processes support enterprises tap into the opportunities of Web 2.0. To ensure an efficient support, this paper presents an approach for identifying an enterprise’s business processes that could be converted into social. Indeed, not all processes are eligible for this conversion. The approach is built upon the concept of enterprise architecture that sheds the light on the necessary contextual elements for guiding the socialization of business processes. Due to the fuzzy nature of socialization, two multi-criteria decision making techniques, fuzzy logic and fuzzy-decision tree, enhance this guidance. A set of experiments for evaluating the proposed approach are presented in the paper, as well.

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.

Various problems are just rising with regard to mining in massive datasets, among which finding similar documents can be pinpointed. The Shingling method converts this problem to a set-based problem. Some of existing methods have used min-hashing to compress the results already driven from the shingling method and then have exploited LSH method to find candidate pairs for similarity search from all pairs of documents. In this paper, an apriori-based method is proposed for finding similar documents based on frequent itemset mining approach. To this end, the apriori algorithm is modified and is customized for similarity search problem. Modeling the similarity search problem as a frequent pattern mining problem, using a modified version of apriori, and dynamic selection the minimum support threshold are the most important advantages of the proposed method, which lead to its appropriate execution time and high quality results. The proposed method finds similar documents in less time than the combined method and MCVM method because it generates fewer candidate pairs for finding similar documents. Furthermore, experimental results show the high quality of the answers of the proposed methods.

In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.

It is very common to use linguistic term scales, whose terms are previously associated with different fuzzy numbers to assign probabilities to events in decision-making processes. However, the rigidity of such scales generates bias in the probability elicitation process and does not allow experts to adequately express their probabilistic judgments. We propose a betting and lottery-based method for eliciting a fuzzy number from the expert that represents his/her probabilistic judgments for a given event, along with a quality measure of the probabilistic judgments based on precision and consistency measures. We also enact a simulation process to analyze possible biases in the proposed fuzzy probability elicitation method.

Vagueness in scientific studies poses a challenge. Intuitionistic Fuzzy Set (IFS) theory has emerged as a powerful and flexible tool to counter such challenge. So, there is a need to develop such measures which can not only measure the vagueness but also quantify the differences in underlying IFSs. The aim of this communication is to introduce one such divergence measure called Intuitionistic Fuzzy Jensen-Tsalli Divergence measure in the settings of IFS theory. The presence of parameters makes the proposed divergence measure flexible and competent for applications. Besides discussing some of its major properties, the findings are applied in pattern recognition problem and in medical diagnosis of some diseases with same set of symptoms. The performance of the proposed measure is genuinely compared with some other existing measures in literature through numerical examples based on medical diagnosis and pattern recognition.

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.