Showing papers in "Information Fusion in 2018"

TL;DR: This survey comprehensively survey the existing methods and applications for the fusion of infrared and visible images, which can serve as a reference for researchers inrared and visible image fusion and related fields.

TL;DR: The emerging researches of deep learning models for big data feature learning are reviewed and the remaining challenges of big data deep learning are pointed out and the future topics are discussed.

TL;DR: This survey paper presents a systematic review of the DL-based pixel-level image fusion literature, summarized the main difficulties that exist in conventional image fusion research and discussed the advantages that DL can offer to address each of these problems.

TL;DR: An updated taxonomy of Dynamic Selection techniques is proposed based on the main characteristics found in a dynamic selection system, and an extensive experimental analysis, considering a total of 18 state-of-the-art dynamic selection techniques, as well as static ensemble combination and single classification models.

TL;DR: A systematic review of the SR-based multi-sensor image fusion literature, highlighting the pros and cons of each category of approaches and evaluating the impact of these three algorithmic components on the fusion performance when dealing with different applications.

TL;DR: A LGDM consensus model in which the clusters are allowed to change and the decision makers provide preferences using fuzzy preference relations is proposed, and an emergency decision to choose a rescue plan is illustrated to validate the proposed method and demonstrate distinctive characteristics compared with the existing approaches.

TL;DR: A novel feedback mechanism is activated to generate recommendation advices for the inconsistent users to increase the group consensus degree, and its novelty is that it produces the boundary feedback parameter based on the minimum adjustment cost optimisation model.

TL;DR: A review of the framework and formulation of opinion dynamics as well as some basic models, extensions, and applications are presented and several open problems are proposed for future research.

TL;DR: A multi-expert multi-criteria decision making method to solve the innovative product design selection problem by developing an enhanced QFD method combined with the complicated fuzzy linguistic representation model, the probabilistic linguistic term set (PLTS), and the ranking method, ORESTE is proposed.

TL;DR: Autoencoders (AEs) as mentioned in this paper have emerged as an alternative to manifold learning for conducting nonlinear feature fusion, and they can be used to generate reduced feature sets through the fusion of the original ones.

TL;DR: An algorithm for lung nodule classification that fuses the texture, shape and deep model-learned information (Fuse-TSD) at the decision level is proposed and evaluated against three approaches on the LIDC-IDRI dataset.

TL;DR: It is concluded that the high-dimensionality of n-gram features and temporal nature of sentiments in long product reviews are the major challenges in sentiment mining from text.

TL;DR: Unlike the canonical VIKOR ranking procedure, this paper provides a new way to rank candidate alternatives and determine the compromise solution depending on distinct preference structures for adapting to the particularities within the Pythagorean fuzzy environment.

TL;DR: Experimental results demonstrate that the proposed self-contained inertial/magnetic sensor based method is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1% distance error and end-to-end position error.

TL;DR: This work enumerate and analyze two alternative methodologies that may be found both in the specialized literature and in standard Machine Learning libraries for Big Data, providing an introduction of the characteristics of these methodologies as well as giving some guidelines for the design of novel algorithms in this field of research.

TL;DR: A comparison of the state-of-the-art in ensembles of multi-label classifiers over a wide set of 20 datasets is carried out, evaluating their performance based on the characteristics of the datasets such as imbalance, dependence among labels and dimensionality.

TL;DR: A comprehensive and meticulous investigation of the reliability theory of MWSNs which was developed in recent years is presented and the existing methods are systematically compared and the advantages and disadvantages of the methods are presented.

TL;DR: The concept and characteristics of HFLTSs are revisited at first and then the recent developments are reviewed and classified according to their computational strategies, and a comparative analysis of some similar techniques is presented to recognize the role of H FLTSs in linguistic decision making.

TL;DR: A decomposition theorem for hesitant fuzzy sets is proved, which states that every typical hesitant fuzzy set on a set can be represented by a well-structured family of fuzzy sets on that set.

TL;DR: This paper develops a novel consensus reaching process for multiple attribute group decision making (MAGDM) with hesitant fuzzy linguistic term sets (HFLTSs), and develops a minimum adjustment distance consensus rule and aggregation model.

TL;DR: Recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way are presented and methods are described in a general way so they can be applied to other information fusion problems as well.

TL;DR: A methodology for the estimation of the main parameters of such schemes, based on a statistical analysis of the unprotected templates, is presented and the soundness of the estimation methodologies is confirmed for face, iris, fingerprint and fingervein over two totally different sets of publicly available databases.

TL;DR: Results imply that CP-HopCM and TT-HOPCM are potential for big data clustering in IoT systems with low-end devices since they can achieve a high compression rate for heterogeneous samples to save the memory space significantly without a significant clustering accuracy drop.

TL;DR: This work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting and predicts emotions based on-body sensors and environmental data.

TL;DR: This paper proposes to empower neutrality by characterizing the boundary between positive and negative reviews, with the goal of improving the model’s performance, and concludes that neutrality is key for distinguishing between negative and negative and for improving sentiment classification.

TL;DR: An introduction to Multiple Classifier Systems including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods is provided.

TL;DR: A new linguistic group decision model called the maximum support degree model (MSDM) is proposed, aiming at maximizing the support degree of the group opinion as well as guarantying the accuracy of thegroup opinion.

TL;DR: This work proposes the use of a multi-view stacking method to fuse the data from heterogeneous types of sensors for activity recognition, and uses sound and accelerometer data collected with a smartphone and a wrist-band while performing home task activities.

TL;DR: HesMCC is presented, which takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance of TCCA, KMUDA, MCCA and LapMCC for multiview dimension reduction.

TL;DR: A novel multi-focus image fusion algorithm is presented in which the task of detecting the focused regions is achieved using a Content Adaptive Blurring (CAB) algorithm, which induces non-uniform blur in a multi- focus image depending on its underlying content.