Topic
Fuzzy number
About: Fuzzy number is a research topic. Over the lifetime, 35606 publications have been published within this topic receiving 972544 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: This paper develops an extended QUALIFLEX method for handling multiple criteria decision-making problems in the context of interval type-2 fuzzy sets and proposes the concordance/discordance index, the weighted concordances/discords index, and the comprehensive concords index as evaluative criteria of the chosen hypothesis for ranking the alternatives.
192 citations
TL;DR: The MULTIMOORA method is extended with type-2 fuzzy sets viz. generalized interval-valued trapezoidal fuzzy numbers to provide the means for multi-criteria decision making related to uncertain assessments.
Abstract: Multi criteria decision making (MCDM) often involves uncertainty which can be tackled by employing the fuzzy set theory. Type-2 fuzzy sets offer certain additional means for the latter purpose. This paper therefore extends the MULTIMOORA method with type-2 fuzzy sets viz. generalized interval-valued trapezoidal fuzzy numbers. The proposed method thus provides the means for multi-criteria decision making related to uncertain assessments. Utilization of aggregation operators also enables to facilitate group multi-criteria decision making. A numerical example of personnel selection demonstrates the possibilities of application of the proposed method in the field of human resource management and performance management in general.
192 citations
01 Aug 2003
TL;DR: This paper focuses on improving the accuracy and interpretability of inductive linguistic rule learning algorithms in Linguistic Fuzzy Modeling.
Abstract: Overview.- Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview.- Accuracy Improvements Constrained by Interpretability Criteria.- COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy.- Constrained optimization of genetic fuzzy systems.- Trade-off between the Number of Fuzzy Rules and Their Classification Performance.- Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms.- Fuzzy CoCo: Balancing Accuracy and Interpretability of Fuzzy Models by Means of Coevolution.- On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes.- Extending the Modeling Process to Improve the Accuracy.- Linguistic Hedges and Fuzzy Rule Based Systems.- Automatic Construction of Fuzzy Rule-Based Systems: A trade-off between complexity and accuracy maintaining interpretability.- Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy.- Extending the Model Structure to Improve the Accuracy.- A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms.- An Iterative Learning Methodology to Design Hierarchical Systems of Linguistic Rules for Linguistic Modeling.- Learning Default Fuzzy Rules with General and Punctual Exceptions.- Integration of Fuzzy Knowledge.- Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?.
191 citations
11 Oct 1998
TL;DR: This paper briefly review the structure of a type-2 FLS and describes type-reduction in detail, focusing on a center-of-sets type- reducer, and provides some practical approximations to type-Reduction computations for certaintype-2 membership functions.
Abstract: Type-reduction in a type-2 fuzzy logic system (FLS) is an "extended" version of the defuzzification operation in a type-1 FLS. In this paper, we briefly review the structure of a type-2 FLS and describe type-reduction in detail. We focus on a center-of-sets type-reducer, and provide some examples to illustrate it. We also provide some practical approximations to type-reduction computations for certain type-2 membership functions.
191 citations
TL;DR: This paper develops a systematic approach to the assessment of fuzzy association rules by partitioning the data stored in a database into examples of a given rule, counterexamples, and irrelevant data, and evaluation measures are derived from the cardinalities of the corresponding subsets.
Abstract: In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an understanding of the semantic meaning of a fuzzy rule. This aspect has been ignored by most existing proposals, which must therefore be considered as ad-hoc to some extent. In this paper, we develop a systematic approach to the assessment of fuzzy association rules. To this end, we proceed from the idea of partitioning the data stored in a database into examples of a given rule, counterexamples, and irrelevant data. Evaluation measures are then derived from the cardinalities of the corresponding subsets. The problem of finding a proper partition has a rather obvious solution for standard association rules but becomes less trivial in the fuzzy case. Our results not only provide a sound justification for commonly used measures but also suggest a means for constructing meaningful alternatives.
191 citations