PL
 |
EN
Preferencje help
Polish version English version Język
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 6

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
Wyszukiwano:
w słowach kluczowych:  three-way decisions
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Content available remote Three-way Decisions with Rough Membership Functions in Covering Approximation Space
Yang Bin, Hu Bao Qing, Quiao Junsheng
Fundamenta Informaticae
|
2019
|
Vol. 165, nr 2
157--191
EN
Rough membership functions in covering approximation space not only give numerical characterizations of covering-based rough set approximations, but also establish the relationship between covering-based rough sets and fuzzy covering-based rough sets. In this paper, we give a new method to discuss three-way decisions with rough membership functions in covering approximation space. Firstly, we introduce three new types of rough membership functions and study their properties. And then, relationship between a covering and its derived fuzzy β-covering is investigated by using rough membership functions. In addition, we study the relationship among the four types of rough membership functions. Finally, a novel type of graded covering-based rough set model is proposed on the basis of rough membership function. And, as an application, its corresponding three-way decisions in incomplete decision systems are investigated.
2
Content available remote S-approximation Spaces : A Three-way Decision Approach
Shakiba A., Hooshmandasl M. R.
Fundamenta Informaticae
|
2015
|
Vol. 139, nr 3
307--328
EN
In this paper, we investigate properties of the lower and upper approximations of an Sapproximation space under different assumptions for its S operator. These assumptions are partial monotonicity, complement compatibility and functional partial monotonicity. We also extend the theory of three way decisions to non-inclusion relations. Also in this work, a new representation for partial monotone S-approximation spaces, called inflections, is introduced. We will also discuss the computational complexity of representing an S-approximation space in terms of inflection sets. Finally, the usefulness of the introduced concepts is illustrated by an example.
3
Content available remote A Multifaceted Analysis of Probabilistic Three-way Decisions
Deng X., Yao Y.
Fundamenta Informaticae
|
2014
|
Vol. 132, nr 3
291--313
EN
In situations where available information or evidence is incomplete or uncertain, probabilistic two-way decisions/classifications with a single threshold on probabilities for making either an acceptance or a rejection decision may be inappropriate. With the introduction of a third non-commitment option, probabilistic three-way decisions use a pair of thresholds and provide an effective and practical decision-making strategy. This paper presents a multifaceted analysis of probabilistic three-way decisions. By identifying an inadequacy of two-way decisions with respect to controlling the levels of various decision errors, we examine the motivations and advantages of three-way decisions. We present a general framework for computing the required thresholds of a three-way decision model as an optimization problem. We investigate two special cases, one is a decision-theoretic rough set model and the other is an information-theoretic rough set model. Finally, we propose a heuristic algorithm for finding the required thresholds.
4
Content available remote Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets
Yang X., Yao J. T.
Fundamenta Informaticae
|
2012
|
Vol. 115, nr 2/3
157-171
EN
The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose a multiagent DTRS model and express it in the form of three-way decisions. The new model seeks for synthesized or consensus decisions when there aremultiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions.
5
Content available remote A Multiple-category Classification Approach with Decision-theoretic Rough Sets
Liu D., Li T., Li H.
Fundamenta Informaticae
|
2012
|
Vol. 115, nr 2/3
173-188
EN
By considering the levels of tolerance for errors and the cost of actions in real decision procedure, a new two-stage approach is proposed to solve the multiple-category classification problems with Decision-Theoretic Rough Sets (DTRS). The first stage is to change an m-category classification problem (m > 2) into an m two-category classification problem, and form three types of decision regions: positive region, boundary region and negative region with different states and actions by using DTRS. The positive region makes a decision of acceptance, the negative region makes a decision of rejection, and the boundary region makes a decision of abstaining. The second stage is to choose the best candidate classification in the positive region by using the minimum probability error criterion with Bayesian discriminant analysis approach. A case study of medical diagnosis demonstrates the proposed method.
6
Content available remote Two Semantic Issues in a Probabilistic Rough Set Model
Yao Y.
Fundamenta Informaticae
|
2011
|
Vol. 108, nr 3/4
249-265
EN
Probabilistic rough set models are quantitative generalizations of the classical and qualitative Pawlak model by considering degrees of overlap between equivalence classes and a set to be approximated. The extensive studies, however, have not sufficiently addressed some semantic issues in a probabilistic rough set model. This paper examines two fundamental semantics-related questions. One is the interpretation and determination of the required parameters, i.e., thresholds on probabilities, for defining the probabilistic lower and upper approximations. The other is the interpretation of rules derived from the probabilistic positive, boundary and negative regions. We show that the two questions can be answered within the framework of a decision-theoretic rough set model. Parameters for defining probabilistic rough sets are interpreted and determined in terms of loss functions based on the well established Bayesian decision procedure. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rules makes a decision of deferment. The three-way decisions are, again, interpreted based on the loss functions
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.