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

Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology's potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers

An Application of Latent Semantic Analysis for Text Categorization

It is a challenge task to discover major topics from text, which provide a better understanding of the whole corpus and can be regarded as a text categorization problem. The goal of this paper is to apply latent semantic analysis (LSA) approach to extract common factors that representing concepts hidden in a large group of text. LSA involves three steps: the first step is to set up a term-document matrix; the second step is to transform the term frequencies into a term-document matrix using various weighting schemes; the third step performs singular value decomposition (SVD) on the matrix to reduce the dimensionality. The reduced-order SVD is the best k-dimensional approximation to the original matrix. The experiment uses more than fifteen hundreds research paper abstracts from a specific field. Because different factor solutions of the LSA suggest different levels of aggregation, this work examines thirteen solutions in the experiment. The results show that LSA is able to identify not only principle categories, but also major themes contained in the text

A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Online services such as Amazon, Tripadvisor, Ebay, etc., allow users to express sentiments about different products or services. Not only that, in some cases it is also possible to express sentiments about the different features characterizing those products or services. Most users express sentiments about individual features by using numerical values, which sometimes do not allow users to reflect properly what they are meaning and therefore they are misleading. To overcome this key issue and make users’ opinions in online services more comprehensive, a new methodology for representing sentiments using linguistic term sets instead of numerical values is presented. In addition, this methodology will allow to implement importance degrees on the different features characterizing users’ opinions. From both sentiments and importance of the features, the most important opinions for each user is derived via an aggregation step based on the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate the corresponding fuzzy set representations of linguistic terms. Furthermore, the final output of the T1OWA based-search process can easily be interpreted by users because it is always of the same type (fuzzy) and defined in the same domain of the original fuzzy linguistic labels. A case study is presented where the T1OWA operator methodology is used to assess different opinions according to different user profiles

Multicriteria Decision Analysis (MCDA) Methods in Life Cycle Assessment (LCA) : a Comparison of Private Passenger Vehicles

Analogies between the life cycle assessment (LCA) and multicriteria decision analysis (MCDA) methodologies have been discussed as well as LCA as an MCDA problem for resolving the trade-offs between multiple environmental objectives. The objective of this study is to compare a variety of specialized multicriteria methods and knowledge-based methods used to aggregate the results from LCA. The studies were conducted using examples of LCA on private passenger vehicles. The research used two classical methods for multicriteria decision making (AHP and TOPSIS), the method of conventional (crisp) reasoning and Mamdani's method of fuzzy inference. The results demonstrate that among the methods analysed, only crisp reasoning does not provide satisfactory results. (original abstract

Heavy moving averages and their application in econometric forecasting

This paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented