The University of Regina is a public research university located in Regina, Saskatchewan, Canada. Founded in 1911 as a private denominational high school of the Methodist Church of Canada, it began an association with the University of Saskatchewan as a junior college in 1925, and was disaffiliated by the Church and fully ceded to the University in 1934; in 1961 it attained degree-granting status as the Regina Campus of the University of Saskatchewan. It became an autonomous university in 1974. The University of Regina has an enrollment of over 12,000 full and part-time students. The university's student newspaper, The Carillon, is a member of CUP.The University of Regina is well-reputed for having a focus on experiential learning and offers internships, professional placements and practicums in addition to cooperative education placements in 41 programs. This experiential learning and career-preparation focus was further highlighted when, in 2009 the University of Regina launched the UR Guarantee Program, a unique program guaranteeing participating students a successful career launch after graduation by supplementing education with experience to achieve specific educational, career and life goals. Partnership agreements with provincial crown corporations, government departments and private corporations have helped the University of Regina both place students in work experience opportunities and help gain employment post-study. Wikipedia.
Yao Y.,University of Regina
Information Sciences | Year: 2010
The rough set theory approximates a concept by three regions, namely, the positive, boundary and negative regions. 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 rule makes a decision of abstaining. This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set model. The results enrich the rough set theory by ideas from Bayesian decision theory and hypothesis testing in statistics. The connections established between the levels of tolerance for errors and costs of incorrect decisions make the rough set theory practical in applications. © 2009 Elsevier Inc. All rights reserved.
Yao Y.,University of Regina
Fundamenta Informaticae | Year: 2011
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.
Yao Y.,University of Regina |
Yao B.,Liaocheng University
Information Sciences | Year: 2012
We propose a framework for the study of covering based rough set approximations. Three equivalent formulations of the classical rough sets are examined by using equivalence relations, partitions, and σ-algebras, respectively. They suggest the element based, the granule based and the subsystem based definitions of approximation operators. Covering based rough sets are systematically investigated by generalizing these formulations and definitions. A covering of universe of objects is used to generate different neighborhood operators, neighborhood systems, coverings, and subsystems of the power set of the universe. They are in turn used to define different types of generalized approximation operators. Within the proposed framework, we review and discuss covering based approximation operators according to the element, granule, and subsystem based definitions. © 2012 Elsevier Inc. All rights reserved.
Carleton R.N.,University of Regina
Journal of Anxiety Disorders | Year: 2016
The current review and synthesis serves to define and contextualize fear of the unknown relative to related constructs, such as intolerance of uncertainty, and contemporary models of emotion, attachment, and neuroticism. The contemporary models appear to share a common core in underscoring the importance of responses to unknowns. A recent surge in published research has explored the transdiagnostic impact of not knowing on anxiety and related pathologies; as such, there appears to be mounting evidence for fear of the unknown as an important core transdiagnostic construct. The result is a robust foundation for transdiagnostic theoretical and empirical explorations into fearing the unknown and intolerance of uncertainty. © 2016 Z.
Carleton R.N.,University of Regina
Expert Review of Neurotherapeutics | Year: 2012
Modern anxiety disorder models implicitly include intolerance of uncertainty (IU) as a critical component for the development and maintenance of these pervasive social and economic concerns. IU represents, at its core, fear of the unknown -a long-recognized, deep-seated fear identified in normative and pathological samples. Indeed, the intrinsic nature of IU can be argued as evolutionarily supported, a notion buttressed by initial biophysiological evidence from uncertainty-related research. Originally thought to be specific to generalized anxiety disorder, recent research has clearly demonstrated that IU is a broad transdiagnostic dispositional risk factor for the development and maintenance of clinically significant anxiety. The available evidence suggests that theorists, researchers and clinicians may benefit from explicitly incorporating IU into models, research designs, case conceptualizations and as a treatment target. © 2012 Expert Reviews Ltd.