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Angelov P.,Lancaster University | Yager R.,Iona College
Information Sciences | Year: 2013

A new data fusion operator based on averaging that is weighted by the density of each particular data sample is introduced in this paper. The proposed approach differs from other weighted averages by its suitability to on-line, real-time applications due to the fact that recursive calculations are being used. It also differs by the fact that it is non-parametric. The proposed operator has a very wide area of possible applications same as the traditional average and most of the other weighted averages. This includes, but is not limited to clustering, classification, pattern recognition, group decision making approaches, data fusion, etc. Some illustrative numerical examples are provided mainly as a proof of concept, including its application to classification. Two simple, yet very effective classification approaches based on the density-based weights called 'one-rule-per-class' or 1R/C and on the minimum distance to weighted class mean has been introduced. Further work will focus on more application-oriented studies that cover various practical applications to clustering and use of different distance measures. © 2012 Elsevier Inc. All rights reserved.


Yager R.R.,Iona College
International Journal of Fuzzy System Applications | Year: 2011

This article discusses the basic features of information provided in terms of possibilistic uncertainty. It points out the entailment principle, a tool that allows one to infer less specific from a given piece of information. The problem of fusing multiple pieces of possibilistic information is and the basic features of probabilistic information are described. The authors detail a procedure for transforming information between possibilistic and probabilistic representations, and using this to form the basis for a technique for fusing multiple pieces of uncertain information, some of which is possibilistic and some probabilistic. A procedure is provided for addressing the problems that arise when the information to be fused has some conflicts. © 2011, IGI Global.


Beutell N.J.,Iona College
International Journal of Environmental Research and Public Health | Year: 2013

This paper examines differences in work-family conflict and synergy among the four generational groups represented in the contemporary workforce: Generation Y Generation X, Baby Boomers, and Matures using data from the 2008 National Study of the Changing Workforce (n = 3,502). Significant generational differences were found for work-family conflict (work interfering with family and family interfering with work) but not for work-family synergy. Mental health and job pressure were the best predictors of work interfering with family conflict for each generational group. Work-family synergy presented a more complex picture. Work-family conflict and synergy were significantly related to job, marital, and life satisfaction. Implications and directions for future research are discussed. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


Yager R.R.,Iona College
Soft Computing | Year: 2013

We introduce the concept of a membership modification function and describe its role in transforming one fuzzy set into another. We discuss the related idea of a membership modification program consisting of a collection of related membership modification functions and show how the concept of level sets is an example of a membership modification program. Using this idea of membership modification allows us to consider other transformations of fuzzy sets that soften the idea of level sets. Using these ideas we provide an extension of the Jaccard similarity index. © 2012 Springer-Verlag.


Angelov P.,Lancaster University | Yager R.,Iona College
International Journal of General Systems | Year: 2012

Over the last quarter of a century, two types of fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi-Sugeno type. They use the same type of scalar fuzzy sets defined per input variable in their antecedent part which are aggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed. Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept. © 2012 Taylor and Francis Group, LLC.

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