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A NUMERICAL APPROACH TO UNCERTAINTY IN ROUGH LOGIC
2013
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
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For this purpose, we introduce a kind of numerical approach to the study of rough logic in this paper. ...
Numerical characterizations of rough sets such as accuracy measure, roughness measure, etc, which aim to quantify the imprecision of a rough set caused by its boundary region, have been extensively studied ...
Acknowledgements We would like to thank the anonymous referees for their valuable comments and suggestions for improving this paper. ...
doi:10.1142/s0218488513500207
fatcat:k5sqrjmx6zaopaxd5ip5t3bbxm
Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations
[chapter]
2013
Lecture Notes in Computer Science
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Introduction & Motivation Modeling uncertain concepts in description logics (DLs) is generally done via numerical approaches probabilistic approach possibilistic approach Drawback: uncertainty is introduced ...
to a set, a (rough) membership function can be defined it can be considered a numerical measure of the uncertainty
Definition (Rough Membership Function) Let C = {F 1 , . . . , F m } be a context and ...
doi:10.1007/978-3-642-35975-0_16
fatcat:k6acwalglfcfnetgxxv4kx5etq
A first-order logic for reasoning under uncertainty using rough sets
1994
Journal of Intelligent Manufacturing
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The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. ...
We develop a set of symbolic truth values based upon rough sets which may be used to augment predicate logic, and provide methods for combining these truth values so that they may be propagated when augmented ...
It has been extended to permit logical inference by McLeish [24] and Saffiotti [37] . All of the approaches mentioned above use numerical quanitifiers. ...
doi:10.1007/bf00123694
fatcat:s6zcaxxwbbdjbbva7cuczlonqa
A rough set approach to reasoning under uncertainty
1995
Journal of experimental and theoretical artificial intelligence (Print)
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In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion ...
The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. ...
A logic for rough reasoning The ideas introduced in the previous section can be adapted to create a quantified logic in which rough sets are used to model upper and lower bounds on the value of the propositions ...
doi:10.1080/09528139508953805
fatcat:3stdindoyrab5htq26qrmxzciy
Subject index to volume 2
1988
International Journal of Approximate Reasoning
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, 328 Bayesian analysis, decision tree induction system and, 330 Bayesian approach, heuristic, to knowledge acquisition, application to analysis of tissuetype plasminogen activator, 342 Bayesian belief ...
uncertainty representations, 228-229 belief in, 339 ...
expert system for, 334
Numeric approaches, to uncertainty manage-
ment, 29--45, 333
Object classification, automatic, fuzzy set the-
ory and rough set theory in, 105-106
Performance, weak predictor ...
doi:10.1016/0888-613x(88)90114-4
fatcat:rj373wy2pzff3d3otqdhxs2gca
Some Views on Information Fusion and Logic Based Approaches in Decision Making under Uncertainty
2010
Journal of universal computer science (Online)
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Decision making under uncertainty is a key issue in information fusion and logic based reasoning approaches. ...
The aim of this paper is to show noteworthy theoretical and applicational issues in the area of decision making under uncertainty that have been already done and raise new open research related to these ...
Conclusions The use of fusion approaches and logic based processing to deal with uncertainty in decision making under uncertainty have provided successful results in the past. ...
doi:10.3217/jucs-016-01-0003
dblp:journals/jucs/XuLMR10
fatcat:xmpnyrflcjcankp7uhi2nvhvwq
Page 2196 of Mathematical Reviews Vol. , Issue 2000c
[page]
2000
Mathematical Reviews
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Petry and Gurdial Arora, Information measures for rough and fuzzy sets and application to uncertainty in relational databases (200-214); Robert E. ...
On the other hand, the theory of rough sets (a tool for handling uncertainties arising from granularity in the domain of discourse, i.e., from the indiscernibility relation between objects in a set) has ...
Survey of Rough and Fuzzy Hybridization
2007
IEEE International Fuzzy Systems conference proceedings
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This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. ...
However, continuing developments of rough and fuzzy extensions to clustering, neurocomputing, and genetic algorithms make hybrid approaches in these areas a potentially rewarding research opportunity as ...
SUPERVISED LEARNING AND INFORMATION RETRIEVAL In [59] a fuzzy-rough nearest neighbor classification approach is presented that attempts to handle both fuzzy uncertainty (caused by overlapping classes ...
doi:10.1109/fuzzy.2007.4295352
dblp:conf/fuzzIEEE/LingrasJ07
fatcat:vygojaq46zb6zej6q3jtlna5au
A Linguistic Truth-Valued Uncertainty Reasoning Model Based on Lattice-Valued Logic
[chapter]
2005
Lecture Notes in Computer Science
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The subject of this work is to establish a mathematical framework that provide the basis and tool for uncertainty reasoning based on linguistic information. ...
An algebra model with linguistic terms, which is based on a logical algebraic structure, i.e., lattice implication algebra, is applied to represent imprecise information and deals with both comparable ...
Based on it, a linguistic truth-valued uncertainty reasoning model based on lattice-valued logic is proposed in Section 3 with an illustration. Section 4 comes to the conclusion. ...
doi:10.1007/11539506_35
fatcat:vka6kyt2bbfcnh2v7yzlekxjuu
Managing Information Uncertainty and Complexity in Decision-Making
2017
Complexity
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A new uncertainty measure-based decision-making approach is presented in this paper and applied to a case study. ...
Managing uncertainty is a prerequisite to effective problem-solving and decision-making in complex systems. ...
of methodological approaches and applications of fuzzy-rough set theory including fuzzy logic and fuzzy sets, rough sets, fuzzy logical operators, and fuzzy relations. ...
doi:10.1155/2017/1268980
fatcat:65hsy2nymnf2vgovnwl7eypfzi
Fuzzy Rough Sets And Its Application In Data Mining Field
2015
Zenodo
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Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. ...
The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain ...
for any x U
ROUGH SETS Rough set theory is a new mathematical approach to uncertain knowledge. ...
doi:10.5281/zenodo.34822
fatcat:trxorjgmlraldiptpkr6ks6mte
Page 1106 of American Society of Civil Engineers. Collected Journals Vol. 128, Issue 12
[page]
2002
American Society of Civil Engineers. Collected Journals
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These premises led to the Meier and Barkdoll approach in which the number of pipes with a minimum velocity is maximized for a given number of hydrant tests. ...
Using the approach described in the paper, the sampling con- ditions most influential to model prediction uncertainty should be induced first. ...
Comparative Overview of Rough Set Toolkit Systems for Data Analysis
2019
MATEC Web of Conferences
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This approach is a useful tool that operates on a formal model using relational algebra, elementary operations on finite sets and firstorder logic. ...
The theory of rough sets aims to overcome problems that are caused by uncertainty and lack of precision within the gathered data sets. ...
Starting from 1960, numerous attempts were made to construct a model that would uncertainty and eliminate its effects so as to maintain the appropriate proportions. ...
doi:10.1051/matecconf/201925203019
fatcat:x4qnthjoljdmrm7lpntmdfoajm
Rule-based systems: a granular computing perspective
2016
Granular Computing
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In particular, this paper gives a certain perspective on how to use set theory for management of information granules for rules/rule terms and different types of computational logic for reduction of learning ...
This is due to the fact that granulation in general means decomposition of a whole into several parts. Similarly, each rule consists of a number of rule terms. ...
The numerical truth value expresses a probability of getting one of the binary truth values in probabilistic logic and a membership degree of truth in fuzzy logic as well as a possibility of truth in rough ...
doi:10.1007/s41066-016-0021-6
fatcat:plc3epsbjjatldaqmw3i23nbhm
A history and introduction to the algebra of conditional events and probability logic
1994
IEEE Transactions on Systems, Man and Cybernetics
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This article is meant to serve as an introduction to the following series of papers on various aspects of conditional event algebra and probability logic. ...
But uncertainty concepts are not restricted to randomness. Other numerical uncertainties such as belief and fuzziness will be touched upon. ...
In this regard, one can contrast the approaches in [5] and in [18] . Perhaps, as in the case of multi-valued logics, each conditional logic is suitable for a particular …eld of applications. ...
doi:10.1109/21.328924
fatcat:rrvwdlae3nf2diw2n7pkh5s2aq
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