Showing papers in "International Journal of Approximate Reasoning in 2012"

TL;DR: The main results presented herein include a synthesis of the properties of the measures defined so far in the scientific literature; the generalizations proposed naturally lead to additions to the body of the previously known measures, leading to the definition of numerous new measures.

TL;DR: This study finds that the new rough sets degenerate to the original MGRS when the size of neighborhood equals zero, and proposes a new definition of covering reduct to describe the smallest attribute subset that preserves the consistency of the neighborhood decision system.

TL;DR: An efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation, is proposed, which yields in a much less amount of time a feature subset (the approximate reduct).

TL;DR: The incremental approaches for updating the relation matrix are proposed to update rough set approximations and show that the proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach.

TL;DR: By introducing a new notion of complementary neighborhood, some types of neighborhood-related covering rough sets are considered, two of which are firstly defined and some basic properties of the complementary neighborhood are shown.

TL;DR: This paper studies reduction of a fuzzy covering and fusion of multi-fuzzy covering systems based on the evidence theory and rough set theory, by using the method of maximum flow.

TL;DR: The uncertainty measure of the knowledge granularity and rough entropy for probabilistic rough set over two universes by the proposed concept is discussed and the general Shannon entropy of covering-based on universe is defined.

TL;DR: A new correction scheme is introduced, which takes into account uncertain metaknowledge on the source's relevance and truthfulness and that generalizes Shafer's discounting operation, and is led to generalize the unnormalized Dempster's rule to all Boolean connectives.

TL;DR: The experimental results demonstrate that the accuracy of classifiers combined by Dempster's rule is not strongly correlated with the diversity obtained by the four measures, and the correlation between the diversity and the ensemble accuracy made by Proportion and Yager's rules is negative, which is not in favor of the claim that increasing diversity could lead to reduction of generalization error of classifier ensembles.

TL;DR: It is concluded that the Pawlak upper approximation operator is just a topological and matroidal closure operator in Pawlaks matroids, and the relations of four kinds of abstract approximation spaces are examined, which correspond exactly to the Relations of closure systems.

TL;DR: A method for efficiently approximating an arbitrary density function using the MoTBF framework is devised, which is more flexible than existing MTE or MoP-based methods, and it supports an online/anytime tradeoff between the accuracy and the complexity of the approximation.

TL;DR: It is shown that the finite representation in terms of count vectors has a very nice geometrical interpretation, and that the representation in Terms of frequency vectors is tied up with multivariate Bernstein (basis) polynomials.

TL;DR: This paper derives, from a set of assumptions, a metric to evaluate the predictions of credal classifiers, which is related to the decision-maker's degree of risk aversion to the variability of predictions.

TL;DR: The study results of this paper have extended and enriched rough set theory from both operator-oriented and set-oriented points of view.

TL;DR: In this paper, the authors address different ways of conceptualizing action research according to how it seems to relate to its field or subject of study, and discuss the differences and similarities that might decrease confusion and increase fruitful differences and tensions.

TL;DR: A novel interval set approach to induce classification rules from incomplete information table is proposed, in which an interval-set-based model to represent the uncertain concepts is presented, and the related properties of the inclusion relations are proved.

TL;DR: A simpler and elementary method, to avoid the Karush-Kuhn-Tucker theorem and the laborious calculus associated with it and to prove the continuity is used.

TL;DR: The scope of this research is to explore an alternative decision support method using the main aspects of fuzzy logic and grey systems to cope with the uncertainty inherent in medical domain and physicians uncertainty to describe numerically the influences among concepts in medicaldomain.

TL;DR: This paper addresses accuracy and reliability problems of state-of-the-art skin detection methods by proposing a fusion-based method termed Dempster-Shafer-based Skin Detection (DSSD), which uses six prominent skin detection criteria as sources of information (SoI), quantifies their reliabilities (confidences), and then combines their confidences based on the DST of evidence.

TL;DR: The contextual discounting of a belief function is extended and its particular link with the canonical disjunctive decomposition is highlighted, and a general family of correction mechanisms allowing one to weaken the information provided by a source is introduced.

TL;DR: A sound logical foundation is set for the TI method based on the monoidal t-norm based logical system MTL.

TL;DR: The same-decision probability is proposed as a query that can be used as a confidence measure for threshold-based decisions, and a bound on its value is derived using a variable elimination algorithm that is proposed.

TL;DR: This paper contributes to the scanty literature on imprecise probability translations by reanalysis of Budescu et al.'s data on numerical interpretations of the Intergovernmental Panel on Climate Change's verbal probability expressions.

TL;DR: In this study, several detailed algorithmic schemes are presented along with related computational aspects associated with Granular Computing and it is shown how the construction of information granules completed through the use of the principle of justifiable granularity becomes advantageous in the realization of granular fuzzy models and a quantification of the quality of the results of modeling.

TL;DR: Compared to several previous interval-forming methods for the constrained Poisson signal rate, the new method gives an interval with better coverage probability or a simpler construction, and the inferential model provides a post-data predictive measure of uncertainty about the unknown parameter value that is not inherent in other interval-form methods.

TL;DR: A comparative study with the original online procedure shows that E2GK outperforms EGK on different entry data sets and solutions are proposed to limit complexity issues.

TL;DR: This work proposes a probabilistic collaborative filtering model that explicitly represents all items and users simultaneously in the model and shows that the proposed system obtains significantly better results than other collaborative filtering systems.

TL;DR: This paper introduces a new unique vector representative, called the characteristic imset, obtained from the standard imset by an affine transformation, suitable for intended application of linear/integer programming methods to learning BN structure.

TL;DR: The semantics and properties of the relative belief transform are discussed, a probability transformation of belief functions closely related to the classical plausibility transform, and it is proved that relative belief commutes with Dempster's orthogonal sum, meets a number of properties which are the duals of those met by the relative plausibility of singletons.

TL;DR: This work proposes a criterion of IP-coherence based on a generalization of Brier score for IP-forecasts that uses 1-sided, lower and upper, probability forecasts and gives a lexicographic strictly proper IP-scoring rule that is based on Briers score.