A discriminative analysis of approaches to ranking fuzzy numbers in fuzzy decision making

This paper presents a discriminative analysis of approaches to ranking fuzzy numbers in fuzzy decision making based on a comprehensive review of existing approaches. The consistency and effectiveness of the approaches to ranking fuzzy numbers are examined in terms of two objective measures developed, leading to a better understanding of the relative performance of individual approaches in ranking fuzzy numbers. Representative fuzzy numbers are selected for carrying out the comparative study of several typical approaches in ranking fuzzy numbers. Several interesting findings are identified which may be of practical significance to fuzzy decision making in real situations

Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition

Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo

The integration of machine translation and translation memory

We design and evaluate several models for integrating Machine Translation (MT) output into a Translation Memory (TM) environment to facilitate the adoption of MT technology in the localization industry. We begin with the integration on the segment level via translation recommendation and translation reranking. Given an input to be translated, our translation recommendation model compares the output from the MT and the TMsystems, and presents the better one to the post-editor. Our translation reranking model combines k-best lists from both systems, and generates a new list according to estimated post-editing effort. We perform both automatic and human evaluation on these models. When measured against the consensus of human judgement, the recommendation model obtains 0.91 precision at 0.93 recall, and the reranking model obtains 0.86 precision at 0.59 recall. The high precision of these models indicates that they can be integrated into TM environments without the risk of deteriorating the quality of the post-editing candidate, and can thereby preserve TM assets and established cost estimation methods associated with TMs. We then explore methods for a deeper integration of translation memory and machine translation on the sub-segment level. We predict whether phrase pairs derived from fuzzy matches could be used to constrain the translation of an input segment. Using a series of novel linguistically-motivated features, our constraints lead both to more consistent translation output, and to improved translation quality, reflected by a 1.2 improvement in BLEU score and a 0.72 reduction in TER score, both of statistical significance (p < 0.01). In sum, we present our work in three aspects: 1) translation recommendation and translation reranking models that can access high quality MT outputs in the TMenvironment, 2) a sub-segment translation memory and machine translation integration model that improves both translation consistency and translation quality, and 3) a human evaluation pipeline to validate the effectiveness of our models with human judgements

A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting

Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFMLNDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores

Lexicographic Methods for Fuzzy Linear Programming

Fuzzy Linear Programming (FLP) has addressed the increasing complexity of real-world decision-making problems that arise in uncertain and ever-changing environments since its introduction in the 1970s. Built upon the Fuzzy Sets theory and classical Linear Programming (LP) theory, FLP encompasses an extensive area of theoretical research and algorithmic development. Unlike classical LP, there is not a unique model for the FLP problem, since fuzziness can appear in the model components in different ways. Hence, despite fifty years of research, new formulations of FLP problems and solution methods are still being proposed. Among the existing formulations, those using fuzzy numbers (FNs) as parameters and/or decision variables for handling inexactness and vagueness in data have experienced a remarkable development in recent years. Here, a long-standing issue has been how to deal with FN-valued objective functions and with constraints whose left- and right-hand sides are FNs. The main objective of this paper is to present an updated review of advances in this particular area. Consequently, the paper briefly examines well-known models and methods for FLP, and expands on methods for fuzzy single- and multi-objective LP that use lexicographic criteria for ranking FNs. A lexicographic approach to the fuzzy linear assignment (FLA) problem is discussed in detail due to the theoretical and practical relevance. For this case, computer codes are provided that can be used to reproduce results presented in the paper and for practical applications. The paper demonstrates that FLP that is focused on lexicographic methods is an active area with promising research lines and practical implications.Spanish Ministry of Economy and CompetitivenessEuropean Union (EU) TIN2017-86647-

Fuzzy group decision making in a competitive situation

In this paper a group decision making problem in a competitive situation with two opponents is considered. Uncertainty in the score assessment for both opponents of any individual of the group as well as between group members is taken into account by means of fuzzy sets. The individual scores can be obtained either direct or via pairwise comparisons of alternatives. The group scores are then mapped into a fuzzy set of preference orderings using the extension principle. By extending metaga7mes to a fuzzy metagame analysis the possible stable symmetric metaequilibria can be found as well as fuzzy ratings for each of the stable metaequilibria. The highest ranking stable metaequilibrium is then obtained by a fuzzy ranking procedure

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice