Aggregation for Atanassov's intuitionistic and interval valued fuzzy sets: the median operator

Atanassov's intuitionistic fuzzy sets (AIFS) and interval valued fuzzy sets (IVFS) are two generalizations of a fuzzy set, which are equivalent mathematically although different semantically. We analyze the median aggregation operator for AIFS and IVFS. Different mathematical theories have lead to different definitions of the median operator. We look at the median from various perspectives: as an instance of the intuitionistic ordered weighted averaging operator, as a Fermat point in a plane, as a minimizer of input disagreement, and as an operation on distributive lattices. We underline several connections between these approaches and summarize essential properties of the median in different representations.

An adaptive algorithm for finding frequent sets in landmark windows

We consider a CPU constrained environment for finding approximation of frequent sets in data streams using the landmark window. Our algorithm can detect overload situations, i.e., breaching the CPU capacity, and sheds data in the stream to “keep up”. This is done within a controlled error threshold by exploiting the Chernoff-bound. Empirical evaluation of the algorithm confirms the feasibility.

Learning from ordered sets and applications in collaborative ranking

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!/2)6.93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.

Interventions designed to limit gestational weight gain: a systematic review of theory and meta-analysis of intervention components

Limiting gestational weight gain (GWG) to recommended levels is important to optimize health outcomes for mother and baby. Surprisingly, a recent review revealed that theory-based interventions to limit GWG were less effective than interventions that did not report a theory-base; however, strict criteria were used to identify theory-informed studies. We extended this review and others by systematically evaluating the theories of behaviour change informing GWG interventions using a generalized health psychology perspective, and meta-analysing behaviour change techniques reported in the interventions. Interventions designed to limit GWG were searched for using health, nursing and psychology databases. Papers reporting an underpinning theory were identified and the CALO-RE taxonomy was used to determine individual behaviour change techniques. Nineteen studies were identified for inclusion. Eight studies were informed by a behaviour change theory; six reported favourable effects on GWG. Overall, studies based on theory were as effective as non–theory-based studies at limiting GWG. Furthermore, the provision of information, motivational interviewing, behavioural self-monitoring and providing rewards contingent on successful behaviour appear to be key strategies when intervening in GWG. Combining these behaviour change techniques with dietary interventions may be most effective. Future research should focus on determining the exact combination of behaviour change techniques, or which underpinning theories, are most useful for limiting GWG.

A new dempster-shafer theory-based method with fuzzy targets for fuzzy sets ranking

In this paper, a new Fuzzy Set (FS) ranking method (for type-1 and interval type-2 FSs), which is based on the Dempster-Shafer Theory (DST) of evidence with fuzzy targets, is investigated. Fuzzy targets are adopted to reflect human viewpoints on fuzzy ranking. Two important measures in DST, i.e., the belief and plausibility measures, are used to rank FSs. The proposed approach is evaluated with several benchmark examples. The use of the belief and plausibility measures in fuzzy ranking are discussed and compared. We further analyze the capability of the proposed approach in fulfilling six reasonable fuzzy ordering properties as discussed in [9]-[11].

Thermal niche, large-scale movements and implications of climate change for a critically endangered marine vertebrate

Climate change is expected to have a number of impacts on biological communities including range extensions and contractions. Recent analyses of multidecadal data sets have shown such monotonic shifts in the distribution of plankton communities and various fish species, both groups for which there is a large amount of historical data on distribution. However, establishing the implications of climate change for the range of endangered species is problematic as historic data are often lacking. We therefore used a different approach to predict the implications of climate change for the range of the critically endangered planktivourous leatherback turtle (Dermochelys coriacea). We used long-term satellite telemetry to define the habitat utilization of this species. We show that the northerly distribution limit of this species can essentially be encapsulated by the position of the 15°C isotherm and that the summer position of this isotherm has moved north by 330 km in the North Atlantic in the last 17 years. Consequently, conservation measures will need to operate over ever-widening areas to accommodate this range extension.

Recognition from appearance subspaces across image sets of variable scale

Linear subspace representations of appearance variation are pervasive in computer vision. In this paper we address the problem of robustly matching them (computing the similarity between them) when they correspond to sets of images of different (possibly greatly so) scales. We show that the naïve solution of projecting the low-scale subspace into the high-scale image space is inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed “downsampling constraint”. The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The proposed method is evaluated on the problem of matching sets of face appearances under varying illumination. In comparison to the naïve matching, our algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

Face recognition with image sets using manifold density divergence

In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face recognition using image sets. We propose a flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average.

Learning over sets using boosted manifold principal angles (BoMPA)

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.

Learning aggregation weights from 3-tuple comparison sets

An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs. © 2013 IEEE.