查看更多>>摘要：We introduce a Choquet-Sugeno-like operator generalizing many operators for bounded nonnegative functions and monotone measures from the literature, e.g., the Sugeno-like operator, the Lovász and Owen measure extensions, the F-decomposition integral with respect to a partition decomposition system, and others. The new operator is based on concepts of dependence relation and conditional aggregation operators, but it does not depend on α-level sets. We also provide conditions under which the Choquet-Sugeno-like operator coincides with some Choquet-like integrals defined on finite spaces and appeared recently in the literature, e.g., the reverse Choquet integral, the d-Choquet integral, the F-based discrete Choquet-like integral, some version of the CF1F2-integral, the CC-integrals (or Choquet-like Copula-based integral) and the discrete inclusion-exclusion integral. Some basic properties of the Choquet-Sugeno-like operator are studied.
查看更多>>摘要：Recommender system has recently received a lot of attention in the information service community. In many application scenarios, such as Internet of Things (IoTs) environments, item multimodal auxiliary information (such as text and image) can be obtained to expand their feature representation and to increase user satisfaction with recommendations. Motivated by this fact, this paper introduces a novel two-stage embedding model (TSEM), which adequately leverage item multimodal auxiliary information to substantially improve recommendation performance. Specifically, it encompasses two sequential stages: graph convolutional embedding (GCE) and multimodal joint fuzzy embedding (MJFE). In the former, we first generate a bipartite graph for user-item interactions, and then utilize it to construct user and item backbone features via a spatial-based graph convolutional network (SGCN). While in the latter, by employing item multimodal auxiliary information, we integrate multi-task deep learning, deterministic Softmax, and fuzzy Softmax into a convolutional neural network (CNN)-based learning framework, which is optimized to obtain user backbone features and item semantic-enhanced fuzzy (SEF) features accurately. After TSEM converges, user backbone features and item SEF features can be utilized to calculate user preferences on items via Euclidean distance. Extensive experiments over two real-world datasets show that the proposed TSEM model significantly outperforms the state-of-the-art baselines in terms of various evaluation metrics.
查看更多>>摘要：Feature selection plays a crucial role in machine learning and data mining, and improves the performance of learning models by selecting a distinguishing feature subset and eliminating irrelevant features. Existing feature selection methods are mainly used for single-label learning and multi-label learning; however, there are only a few feature selection methods for label distribution learning. Label distribution learning has the “curse of dimensionality” problem, similar to that in multi-label learning. In label distribution learning, the related labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms can not be directly applied to label distribution data, and discretizing the label distribution data into multi-label data would result in the loss of some important supervised information. To solve this problem, a novel feature selection algorithm for label distribution learning is proposed in this paper. The proposed method utilizes neighborhood granularity to explore feature similarity, and it uses a correlation coefficient to generate the label correlations. In addition, sparse learning is used to improve the robustness and control complexity. Experimental results indicate that our proposed method is more effective than five state-of-art feature selection algorithms on twelve datasets, with respect to six representative evaluation measures.
查看更多>>摘要：In research, we found that the purpose of most trackers is to obtain an accurate and robust score map, neglecting how to further examine the confidence of the results. Inspired by the Siamese trackers, which merely use the template from the first frame to locate the target, we propose a novel object-uncertainty policy. Firstly, we propose a dynamic design of the target template set for the tracked target, considering the initial target template and the reliable target template of the subsequent frames concurrently. Secondly, we adopt the multi-layer fusion to represent the target while analyzing the fusion of various feature layers. Moreover, we use a more effective cosine similarity function to calculate the similarity instead of the correlation operation. Finally, we propose a novel voting mechanism in accordance with the similarity between the target tracked in subsequent frames and the target template set. More importantly, this method can be embedded into DCF-like methods to improve tracking performance, which is embedded into the recent DiMP and PrDiMP trackers separately for comparison. Extensive experiments demonstrate that the discriminative ability of the model can be enhanced effectively by using our proposed method, capable of preventing the model from learning the background information. The code and raw tracking results are available at https://github.com/hexdjx/OUPT.
查看更多>>摘要：Ontology verification refers to the activity where an ontology is tested against its ontology requirements to ensure that it is built correctly in compliance with its ontology requirements specification. Therefore, it is an important activity that should be performed in any ontology development process. Since manual verification can be a time-consuming and repetitive task, testing processes to automatically verify an ontology facilitate this activity. Moreover, the involvement of not only ontology engineers during the ontology verification process, but also domain experts and users, can provide valuable feedback to avoid misunderstandings and lack of information. This paper proposes a method for ontology verification that defines the testing activities to be performed. The method uses a testing language based on lexico-syntactic patterns to facilitate the definition of tests and an ontology to store and publish such tests. Moreover, this verification testing method proposes an online tool to execute tests on one or more ontologies. The method was compared in terms of time and errors by user evaluation with other tools for ontology verification; the evaluation showed that the tools that use testing languages had better results in terms of reducing errors in the verification activity compared to the tools that do not.
查看更多>>摘要：As a complicated cognitive process, multi-attribute decision-making usually focuses on the decision-making issue of seeking the optimal alternative or ranking alternatives under the framework of multiple attributes. The three-way decision approach with the delayed decision can more effectively reduce decision risks than traditional two-way counterparts for multi-attribute decision-making. In this article, we aim to put forward a novel three-way multi-attribute decision-making model in light of a probabilistic dominance relation with intuitionistic fuzzy sets. First, we investigate the three-way multi-attribute decision-making in light of a probabilistic dominance relation in an intuitionistic fuzzy information system. Second, we derive the conditional probability of the intuitionistic fuzzy set. Third, we evaluate the part supplier selection via the constructed model. At last, for the sake of showing the validity and applicability of the constructed three-way intuitionistic fuzzy multi-attribute decision-making model, we further perform extensive comparative analysis along with experimental analysis from diverse perspectives.
查看更多>>摘要：With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed.
查看更多>>摘要：Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI.
查看更多>>摘要：This paper is concerned with the problem of developing more efficient stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems, i.e., not only reducing the conservatism but also (at the same time) alleviating the complexity of fuzzy control synthesis. Firstly, under the framework of traditional fuzzy stabilization, the recent multiple-sums-based method reported in the literature is improved by removing all the redundant variables, and thus the same feasible stabilization range can be obtained at the cost of more economical computational burden. Secondly, in order to further enhance the control efficiency, a dynamic trade-off between conservatism and complexity is established by proposing a new online evaluator of the updated system variation information across two adjacent sampling instants, and thus the so-called resilient stabilization is developed for reducing the conservatism without increasing or even alleviating the complexity. Moreover, all the determined matrices at the farthest sampling instant can be entirely removed without introducing any conservatism due to the proposed substitution technique. Finally, the superiority and effectiveness of our proposed methods are illustrated by a set of simulation comparisons with relevant research results reported in recent literature.
查看更多>>摘要：Shape analogy is a key technique in analyzing time series. That is, time series are compared by how much they look alike. This concept has been applied for many years in geometry. Notably, none of the current techniques describe a time series as a geometric curve that is expressed by its relative location and form in space. To fill this gap, we introduce Shape-Sphere, a vector space where time series are presented as points on the surface of a sphere. We prove a pseudo-metric property for distances in Shape-Sphere. We show how to describe the average shape of a time series set using the pseudo-metric property of Shape-Sphere by deriving a centroid from the set. We demonstrate the effectiveness of the pseudo-metric property and its centroid in capturing the ‘shape’ of a time series set, using two important machine learning techniques, namely: Nearest Centroid Classifier and K-Means clustering, using 85 publicly available data sets. Shape-Sphere improves the nearest centroid classification results when the shape is the differentiating feature while keeping the quality of clustering equivalent to current state-of-the-art techniques.
NSTL
Elsevier