Yingqiu Zhu

• Doctor of Philosophy
• Lecturer at University of International Business and Economics

Rapid developments in third‐party online payment platforms now make it possible to record massive bank card transaction data. Clustering on such transaction data is of great importance for the analysis of merchant behaviours. However, traditional methods based on generated features inevitably lead to much loss of information. To make better use of...

In deep learning tasks, the update step size determined by the learning rate at each iteration plays a critical role in gradient-based optimization. However, determining the appropriate learning rate in practice typically relies on subjective judgement. In this work, we propose a novel optimization method based on local quadratic approximation (LQA...

One-shot-type (or divide-and-conquer) estimators have been widely used for distributed statistical analysis. However, their outstanding statistical efficiency hinges on two critical conditions. The first is the uniformity condition, which requires that the sample sizes allocated to different Workers should be as comparable as possible. The second o...

This paper proposes a novel recommendation methodology to guide visitors to find their proper automobile exhibition halls for auto show. In the proposed method, spatio-temporal features of visitors' behavior are first considered to construct their profiling, and then their interests are extracted based on visitors' clustering. Next, three modules i...

The emergence of massive data in recent years brings challenges to automatic statistical inference. This is particularly true if the data are too numerous to be read into memory as a whole. Accordingly, new sampling techniques are needed to sample data from a hard drive. In this paper, we propose a sequential addressing subsampling (SAS) method, th...

As the economic environment becomes increasingly complex, enhancing supply chain resilience is crucial for the operations and long-term development of enterprises. Real-world supply chains, encompassing components such as goods, warehouses, and plants, often contain complex network structures, making resilience analysis a challenging task. This pap...

Incorrect labeling is a common issue that often occurs in machine learning applications. If datasets contain noisy labels and these errors are not corrected, the performance of the trained classifiers is affected significantly. In order to address this issue, we present a reliability measurement for labels, which is generated based on crowdsourcing...

The emergence of massive data in recent years brings challenges to automatic statistical inference. This is particularly true if the data are too numerous to be read into memory as a whole. Accordingly, new sampling techniques are needed to sample data from a hard drive. In this paper, we propose a sequential addressing subsampling (SAS) method tha...

Labeling mistakes are frequently encountered in real-world applications. If not treated well, the labeling mistakes can deteriorate the classification performances of a model seriously. To address this issue, we propose an improved Naive Bayes method for text classification. It is analytically simple and free of subjective judgements on the correct...

With the rapid development of online payment platforms, it is now possible to record massive transaction data. The economic behaviors are embedded in the transaction data for merchants using these platforms. Therefore, clustering on transaction data significantly contributes to analyzing merchants' behavior patterns. This may help the platforms pro...

Online social network platforms such as Twitter and Sina Weibo have been extremely popular over the past 20 years. Identifying the network community of a social platform is essential to exploring and understanding the users' interests. However, the rapid development of science and technology has generated large amounts of social network data, creat...

In deep learning tasks, the learning rate determines the update step size in each iteration, which plays a critical role in gradient-based optimization. However, the determination of the appropriate learning rate in practice typically replies on subjective judgement. In this work, we propose a novel optimization method based on local quadratic appr...

Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear models on a distributed system with nonrandomly distributed data. We develop a Pseudo-Newton-Raphson algorithm for efficient estimation. In this algorithm, we first obtain a...

Link prediction is one of the most important personalized services in social network platforms. The key point is to predict the probability of the existence of a link between two nodes based on various information in the network. This article combines information of the network structure with the user-generated contents. We propose link prediction...

Spatial computing has emerged a critical issue in emergency management of public health. Due to the complexity of spatial data structure and disperse character of spatio-temporal data, when emergency event of public health occurs, it is difficult to get the needed data and analysis it then make quick decision in a short time. In this paper, OSCAR:...

Due to the rapid development of Web 2.0 technology, the Internet has changed how people communicate. Increasing numbers of customers generate content in online co-creation communities, and this co-creation between customers and companies has become a fashion trend. This study investigates the relationship between social embeddedness and the amount...

Analysis on zoonotic infectious diseases is an important issue in emergency management as it significantly supports governmental and medical decision making when a zoonotic infectious disease outbreaks. To effectively prevent and control the diseases, it is necessary to explore the pathogenesis and identify correlative influence factors. However, l...

Social media makes co-creation process more open and collaborative between enterprises and customers. This paper studies the relationship between customer's social networks, that is, friendship-based and content-based networks, and customer-generated content in company social media platform. For customer's social networks we focus on the network ce...

Satellite tracking technologies enable scientists to collect data of animal migrations and species habitats on a large scale. Modeling distributions of wild animals is of considerable use. It helps researchers to understand important ecological phenomena such as the spread of bird flu and climate changes. Species distribution modeling has been stud...

Credit risk assessment is a crucial process for financial institutions when granting commercial loans. However, the manual analysis of the overall condition of firms through customer due diligence reports is costly for both time and labor. This paper proposes a novel credit risk evaluation approach using GMKL model to automate the decision-making p...