Three-way Investment Decisions with Decision-theoretic Rough Sets
International Journal of Computational Intelligence SystemsPublished 1 February 2011Open access
Dun Liu, Yiyu Yao, Tianrui Li
Citations185
SJR quartileQ2
SJR score0.59
SNIP0.83
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The decision-theoretic rough set model is adopted to derive a profit-based three-way approach to investment decision-making and a case study of oil investment demonstrates the proposed method.
Abstract
The decision-theoretic rough set model is adopted to derive a profit-based three-way approach to investment decision-making. A three-way decision is made based on a pair of thresholds on conditional probabilities. A positive rule makes a decision of investment, a negative rule makes a decision of noninvestment, and a boundary rule makes a decision of deferment. Both cost functions and revenue functions are used to calculate the required two thresholds by maximizing conditional profit with the Bayesian decision procedure. A case study of oil investment demonstrates the proposed method.
Keywords
Computer ScienceEngineering
Pattern classification and scene analysis
12,643 Citations1973Richard O. Duda, Peter E. Hart
International Journal of Parallel ProgrammingRough sets
12,138 Citations1982Zdzis aw Pawlak
The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory and some applications are outlined.
Rough Sets: Theoretical Aspects of Reasoning about Data
8,416 Citations1991Zdzisław Pawlak
Journal of the Royal Statistical Society Series A (General)Pattern Classification and Scene Analysis.
3,240 Citations1974Mairi Clarke, Richard O. Duda +1 more
Communications of the ACMRough sets
3,212 Citations1995Zdzisław Pawlak, Jerzy W. Grzymala‐Busse +2 more
This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal of Computer and System SciencesVariable precision rough set model
2,024 Citations1993Wojciech Ziarko
A generalized model of rough sets called variable precision model (VP-model), aimed at modelling classification problems involving uncertain or imprecise information, is presented and the main concepts are introduced formally and illustrated with simple examples.
Information SciencesThree-way decisions with probabilistic rough sets
1,555 Citations2009Yiyu Yao
This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set models, enriched by ideas from Bayesian decision theory and hypothesis testing in statistics.
Springer series in statisticsSequential Tests of Statistical Hypotheses
1,387 Citations1992Abraham Wald
New England Journal of MedicineThe Threshold Approach to Clinical Decision Making
1,319 Citations1980Stephen G. Pauker, Jerome P. Kassirer
Using the concepts of decision analysis, expressions for two threshold probabilities involved in the choice of whether to withhold treatment, obtain more data by testing, or treat without subjecting the patient to the risks of further diagnostic tests are derived.
International Journal of Man-Machine StudiesA decision theoretic framework for approximating concepts
612 Citations1992Yiyu Yao, S. K. M. Wong
This paper shows that if a given concept is approximated by one set, the same result given by the α-cut in the fuzzy set theory is obtained, and can derive both the algebraic and probabilistic rough set approximations.
International Journal of Approximate ReasoningProbabilistic rough set approximations
591 Citations2007Yiyu Yao
Based on rough membership functions and rough inclusion functions, the Bayesian decision-theoretic analysis is adopted to provide a systematic method for determining the precision parameters by using more familiar notions of costs and risks.
International Journal of Man-Machine StudiesRough sets: probabilistic versus deterministic approach
469 Citations1988Zdzisław Pawlak, S. K. M. Wong +1 more
W: B. Gains and J. Boose, editors, Machine Learning and Uncertain Reasoning 3, pages 227-242, 1990.
Lecture notes in computer scienceDecision-Theoretic Rough Set Models
452 Citations2007Yiyu Yao
It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Lecture notes in computer scienceThree-Way Decision: An Interpretation of Rules in Rough Set Theory
416 Citations2009Yiyu Yao
A new interpretation of rules in rough set theory is introduced, which enables us to derive three types of decision rules, namely, positive rules for acceptance, boundary rules for indecision or delayed decision, and negative rules for rejection.
Journal of Mathematical PsychologyKey Concepts in Model Selection: Performance and Generalizability
382 Citations2000Marc R. Forster
It seems that simplicity and parsimony may be an additional factor in managing these errors, in which case the standard methods of model selection are incomplete implementations of Occam's razor.
Expert SystemsProbabilistic approaches to rough sets
336 Citations2003Yiyu Yao
The Shannon entropy function is used to quantitatively characterize partitions of a universe and both algebraic and probabilistic rough set approximations are studied, both defined in a decision‐theoretic framework.
Fundamenta InformaticaeGame-Theoretic Rough Sets
235 Citations2011Joseph P. Herbert, JingTao Yao
This article investigates the Game-theoretic Rough Set model and its capability of analyzing a major decision problem evident in existing probabilistic rough set models and formulate a learning method using the GTRS model that repeatedly analyzes payoff tables created from approximation measures and modified conditional risk strategies to calculate parameter values.
Fuzzy Sets and SystemsComparison of the probabilistic approximate classification and the fuzzy set model
195 Citations1987S. K. M. Wong, Wojciech Ziarko
It is shown that the generalized notion (probabilistic approximate classification) of rough sets can be conveniently described by the concept of fuzzy sets and argued that there does not exist a universal definition for the fuzzy intersection (union) operation.
Lecture notes in computer scienceRough Sets and Bayes Factor
119 Citations2005Dominik Ślȩzak
The proposed Rough Bayesian model (RB) does not require information about the prior and posterior probabilities in case they are not provided in a confirmable way, and is related to the Bayes factor known from the Bayesian hypothesis testing methods.
Knowledge-Based SystemsAn information filtering model on the Web and its application in JobAgent
118 Citations2000Y. Li, Chengqi Zhang +1 more
A model for information filtering on the Web with rough set decision theory is proposed, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem.
Lecture notes in computer scienceA Three-Way Decision Approach to Email Spam Filtering
111 Citations2010Bing Zhou, Yiyu Yao +1 more
A three-way decision approach to spam filtering based on Bayesian decision theory is introduced, which provides a more sensible feedback to users for precautionary handling their incoming emails, thereby reduces the chances of misclassification.
Proceedings of the VLDB EndowmentBrighthouse
106 Citations2008Dominik Ślȩzak, Jakub Wróblewski +2 more
Additional benefits resulting from Knowledge Grid for compressed, column-oriented databases, including assistance in query optimization and execution, are demonstrated by minimizing the need of data reads and data decompression.
Fundamenta InformaticaeTwo Semantic Issues in a Probabilistic Rough Set Model
99 Citations2011Yiyu Yao
This paper examines two fundamental semantics-related questions: the interpretation and determination of the required parameters, i.e., thresholds on probabilities, for defining the probabilistic lower and upper approximations and the interpretation of rules derived from the Probabilistic positive, boundary and negative regions.
Computers & Mathematics with ApplicationsCriteria for choosing a rough set model
74 Citations2008Joseph P. Herbert, JingTao Yao
This work investigates how two rough set models, the Pawlak model and the probabilistic model, influence the decision goals of a user and presents a list of decision types corresponding to the available information and user needs.
Journal of Environmental ManagementDo statistical inferences allowing three alternative decisions give better feedback for environmentally precautionary decision-making?
63 Citations2006Rob Goudey
This paper shows how a three-alternatives statistical inference about compliance with a percentile standard might be applied to a small number of observations using a non-parametric binomial interval, which results in the sample size requirements for various percentile ranks becoming explicit.
Oil IT Journal.Risk, Uncertainty and Investment Decision-Making in the Upstream Oil and Gas Industry
46 Citations2000Fiona Macmillan
Lecture notes in computer scienceA Multi-View Decision Model Based on Decision-Theoretic Rough Set
42 Citations2009Xianzhong Zhou, Huaxiong Li
A multi-view decision method based on decision-theoretic rough set model is proposed, in which optimistic decision, pessimistic decision, and indifferent decision are provided according to the cost of misclassification.
Two decades'research on decision-theoretic rough sets
41 Citations2010Dun Liu, Huaxiong Li +1 more
The history of DTRSM over the last twenty years is reviewed, including the theories and the potential applications, and three-way decision procedure, attribute reduction methods and potential applications of D TRSM are examined.
An Email Classification Scheme Based on Decision-Theoretic Rough Set Theory and Analysis of Email Security
36 Citations2005Wenqing Zhao, Yongli Zhu
A new scheme based on decision-theoretic rough sets is introduced to classify emails into three categories - spam, no-spam and suspicious, and by comparing with popular classification methods like Naive Bayes classification, the anti-Spam filter model is reduced, and potential security problems of some email systems are found.
Lecture notes in computer scienceLearning Optimal Parameters in Decision-Theoretic Rough Sets
31 Citations2009Joseph P. Herbert, JingTao Yao
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented, using game theory to maximize two classification measures and a learning method to reformulate the loss functions.
Lecture notes in computer scienceMultiple-Category Classification with Decision-Theoretic Rough Sets
22 Citations2010Dun Liu, Tianrui Li +2 more
Two stages with bayesian decision procedure are proposed to solve the multiple-category classification problems and the best candidate rules in positive region are chosen by using the minimum probability error criterion with bayes decision theory.
Energy EconomicsProduction-sharing contracts and decision-making in oil production
22 Citations1993Purnomo Yusgiantoro, Frank S.T. Hsiao
Three-way Decision-theoretic Rough Sets
8 Citations2011Tianrui Li
A model of three-way decision-theoretic rough sets (DTRS) was presented based on the Bayesian decision theory and a detailed formulation of DTRS was given for solving the practical decision problems.
In search for effective granularity with DTRS
5 Citations2010Bing Zhou, Yiyu Yao
This paper introduces an adaptive learning method that automatically deals with the deferred cases by searching for the effective granularity in a decision tree that is illustrated by a given example.