25,733 research outputs found
Probabilistic soft sets and dual probabilistic soft sets in decision making with positive and negative parameters
In this paper, we motivate and introduce probabilistic soft sets and dual probabilistic soft sets for handling decision making problem in the presence of positive and negative parameters. We propose several types of algorithms related to this problem. Our procedures are flexible and adaptable. An example on real data is also give
Intertemporal Choice of Fuzzy Soft Sets
This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie
Probabilistic Kernel Support Vector Machines
We propose a probabilistic enhancement of standard kernel Support Vector
Machines for binary classification, in order to address the case when, along
with given data sets, a description of uncertainty (e.g., error bounds) may be
available on each datum. In the present paper, we specifically consider
Gaussian distributions to model uncertainty. Thereby, our data consist of pairs
, , along with an indicator
to declare membership in one of two categories for each pair.
These pairs may be viewed to represent the mean and covariance, respectively,
of random vectors taking values in a suitable linear space (typically
). Thus, our setting may also be viewed as a modification of
Support Vector Machines to classify distributions, albeit, at present, only
Gaussian ones. We outline the formalism that allows computing suitable
classifiers via a natural modification of the standard "kernel trick." The main
contribution of this work is to point out a suitable kernel function for
applying Support Vector techniques to the setting of uncertain data for which a
detailed uncertainty description is also available (herein, "Gaussian points").Comment: 6 pages, 6 figure
Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise
Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a
worth pondering question. To help those traditional businesses that have no experience to dispel the clouds
and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take
them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses
the combination of several linguistic terms and their proportions to reveal decision information by opposite
angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs)
based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the
comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover,
it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the
DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under
the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering
the similarity among the EGRA, TODIM, and VIKOR.Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199Scientific Research Foundation of the Graduate School of Southeast University under Grant
YBJJ1832FEDER Financial Support under Grant TIN2016-75850-
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