IEEE Transactions on Knowledge and Data Engineering

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source domain to understand a similar yet distinct unlabeled target domain. Notably, global distribution statistics across domains and local semantic characteristics across ...

Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be ...

Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a <italic>graph and attentive multi-path convolutional network</italic> (...

Complicated temporal patterns can provide important information for accurate time series forecasting. Existing long short-term memory (LSTM) model with attention mechanism have achieved significant performance. However, the exponential decay of long-term ...

In recent years, context-aware recommender systems (CARSs), which incorporate contextual information to achieve better recommendations, become a hot topic in the domain of recommender systems. Many context-aware recommendation methods have been proposed ...

Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of coping with overfitting, dropout is one of the representative ways. From ...

One of the major problems in real-life multiblock dynamic data analysis is that all the available modalities may not be relevant. Some of them may provide noisy or even inconsistent information with respect to other modalities. So, it is necessary to ...

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model ...

Many binary class datasets in real-life applications are affected by class imbalance problem. Data complexities like noise examples, class overlap and small disjuncts problems are observed to play a key role in producing poor classification performance. ...

Prediction over tabular data is often a crucial task in many real-life applications. Recent advances in deep learning give rise to various deep models for tabular data prediction. A common and essential step in these models is to vectorize raw input ...

Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings. First, they are essentially low-pass filters, thus the potentially useful ...

Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such ...

This article is on designing a compact data structure for multi-set membership testing that allows fast set querying. Multi-set membership testing is a fundamental operation for computing systems. Most existing schemes for multi-set membership testing are ...

Fine-grained urban crime prediction is of great significance to urban management and public safety. Previous crime prediction work has been done at a relatively coarse time granularity, which may suffer from two issues for fine-grained crime prediction. 1)...

Geospatial data provides a lot of benefits for personalized services. However, since the geospatial data contains sensitive information about personal activities, collecting the raw data has a potential risk of leaking private information from the ...

With the deep penetration of the Internet and mobile devices, preference rankings are being collected on a massive scale by diverse data collectors for various business demands. However, users&#x2019; preference rankings in many applications are highly ...

Phrase mining is an essential step when transforming unstructured text into structured information, in which the aim is to extract high-quality phrases from given corpora automatically. Existing statistics-based methods have achieved state-of-the-art ...

Many applications rely on continual data collection to provide real-time information services, e.g., real-time road traffic forecasts. However, the collection of original data brings risks to user privacy. Recently, local differential privacy (LDP) has ...

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-modal hashing methods have attracted considerable attention from the researchers. For the supervised cross-modal hashing methods, how to make the learned hash ...

Seeking an efficient solution for the problem of dynamic user preferences on social networks is challenging because the input data are <italic>short texts</italic> and user preferences usually <italic>change</italic> over time. This work proposes a novel ...

Time Series Classification (TSC) is a crucial area in machine learning. Although applications of Deep Neural Networks (DNNs) in this area have led to relatively good results, classifying this kind of data is a major challenge. This issue is due to the ...

A cross join between two attribute sets holds on a relation whenever its projection onto the union of the attribute sets is the cross join between its projections on the first and second attribute set. Hence, the cross join is a fundamental operator on ...

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we ...

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent ...

Multi-view clustering has attracted a lot of attention due to its ability to integrate information from distinct views, but how to improve efficiency is still a hot research topic. Anchor graph-based methods and k-means-based methods are two current ...

Using hashing to learn the binary codes of users and items significantly improves the efficiency and reduces the space consumption of the recommender system. However, existing hashing-based recommender systems remain black boxes without any explainable ...

Graph neural network (GNN) has shown its prominent performance in representation learning of graphs but it has not been fully considered for heterogeneous graphs which contain more complex structures and rich semantics. The rich semantic information of ...

Co-clustering methods make use of the correlation between samples and attributes to explore the co-occurrence structure in data. These methods have played a significant role in gene expression analysis, image segmentation, and document clustering. In ...

In decision-making systems, how to address uncertainty plays an important role for the improvement of system performance in uncertainty reasoning. Dempster&#x2013;Shafer evidence (DSE) theory is an effective method to address uncertainty in decision-...

Directional distribution analysis has long served as a fundamental functionality in abstracting dispersion and orientation of spatial datasets. Spatial datasets that describe sensitive information of individuals such as health status and home addresses ...