A Fuzzy Approach to the Measurement of Leakages for North American Health Systems

This paper uses a fuzzy-fuzzy stochastic dominance approach to compare patients’ leakages in the Canadian and the U.S. health care systems. Leakages are defined in terms of individuals who are in bad health and could not have access to health care when needed. To carry his comparison we rely on the assumption that Canada is a strong counterfactual for the U.S. We first develop a class of fuzzy leakages indices and incorporate them in a stochastic dominance framework to derive the dominance criterion. We then use the derived criterion to perform inter-country comparisons on the global level. To provide more insight, we decompose the analysis with respect to gender, ethnicity, income and education. Intra-country comparisons reveal the presence of income based leakage inequalities in both countries yet, gender, ethnic and education based disparities appear to be present in the U.S. only. As for inter-country comparisons, results are in general consistent with the hypothesis that leakages are less important under the Canadian health care system.Health care resources, Fuzzy sets, Leakage

A Fuzzy Approach to the Measurement of Leakages for North American Health Systems

This paper uses a fuzzy-fuzzy stochastic dominance approach to compare patients' leakages in the Canadian and the U.S health care systems. Leakages are defined in terms of individuals who are in bad health and could not have access to health care when needed. To carry this comparison we rely on the assumption that Canada is a strong counterfactual for the U.S. We first develop a class of fuzzy leakages indices and incorporate them in a stochastic dominance framework to derive the dominance criterion. We then use the derived criterion to perform inter-country comparisons on the global level. To provide more insight, we decompose the analysis with respect to gender, ethnicity, income and education. Intra-country comparisons reveal the presence of income based leakage inequalities in both countries yet, gender, ethnic and education based disparities appear to be present in the U.S only. As for inter-country comparison, results are in general consistent with the hypothesis that leakages are less important under the Canadian health care system.Health care resources, Fuzzy sets, Leakage

Unsharp Quantum Reality

The positive operator (valued) measures (POMs) allow one to generalize the notion of observable beyond the traditional one based on projection valued measures (PVMs). Here, we argue that this generalized conception of observable enables a consistent notion of unsharp reality and with it an adequate concept of joint properties. A sharp or unsharp property manifests itself as an element of sharp or unsharp reality by its tendency to become actual or to actualize a specific measurement outcome. This actualization tendency-or potentiality-of a property is quantified by the associated quantum probability. The resulting single-case interpretation of probability as a degree of reality will be explained in detail and its role in addressing the tensions between quantum and classical accounts of the physical world will be elucidated. It will be shown that potentiality can be viewed as a causal agency that evolves in a well-defined way

A dissimilarity-based approach for Classification

The Nearest Neighbor classifier has shown to be a powerful tool for multiclass classification. In this note we explore both theoretical properties and empirical behavior of a variant of such method, in which the Nearest Neighbor rule is applied after selecting a set of so-called prototypes, whose cardinality is fixed in advance, by minimizing the empirical mis-classification cost. With this we alleviate the two serious drawbacks of the Nearest Neighbor method: high storage requirements and time-consuming queries. The problem is shown to be NP-Hard. Mixed Integer Programming (MIP) programs are formulated, theoretically compared and solved by a standard MIP solver for problem instances of small size. Large sized problem instances are solved by a metaheuristic yielding good classification rules in reasonable time.operations research and management science;

The Phase Diagram of Scalar Field Theory on the Fuzzy Disc

Using a recently developed bootstrapping method, we compute the phase diagram of scalar field theory on the fuzzy disc with quartic even potential. We find three distinct phases with second and third order phase transitions between them. In particular, we find that the second order phase transition happens approximately at a fixed ratio of the two coupling constants defining the potential. We compute this ratio analytically in the limit of large coupling constants. Our results qualitatively agree with previously obtained numerical results.Comment: 1+17 pages, v2: typos fixed, published versio

Fuzzy transformations and extremality of Gibbs measures for the Potts model on a Cayley tree

We continue our study of the full set of translation-invariant splitting Gibbs measures (TISGMs, translation-invariant tree-indexed Markov chains) for the $q$-state Potts model on a Cayley tree. In our previous work \cite{KRK} we gave a full description of the TISGMs, and showed in particular that at sufficiently low temperatures their number is $2^{q}-1$. In this paper we find some regions for the temperature parameter ensuring that a given TISGM is (non-)extreme in the set of all Gibbs measures. In particular we show the existence of a temperature interval for which there are at least $2^{q-1} + q$ extremal TISGMs. For the Cayley tree of order two we give explicit formulae and some numerical values.Comment: 44 pages. To appear in Random Structures and Algorithm

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System