Tie Li's research while affiliated with University of Electronic Science and Technology of China and other places
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Publications (8)
In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the data that can be used to infer user's behaviors and identify potential risks. Due to the complexity of human behaviors and changing social environments,...
Distance metric learning (DML) aims to learn distance metrics that reflect the interactions between features and labels. Due to the high computational complexity, existing DML models are unsuitable for large-scale datasets. This study proposes a DML approach for large-scale problems by reducing the number of variables, utilizing sparse structures o...
In malicious URLs detection, traditional classifiers are challenged because the data volume is huge, patterns are changing over time, and the correlations among features are complicated. Feature engineering plays an important role in addressing these problems. To better represent the underlying problem and improve the performances of classifiers in...
Due to the popularization of the concept of “new retailing”, we study a new commercial model named O2O (online-to-offline), which is a good combination model of a direct channel and a traditional retail channel. We analyze an O2O supply chain in which manufacturers are responsible for making green products and selling them through both online and o...
Nowadays, datasets are always dynamic and patterns in them are changing. Instances with different labels are intertwined and often linearly inseparable, which bring new challenges to traditional learning algorithms. This paper proposes adaptive hyper-sphere (AdaHS), an adaptive incremental classifier, and its kernelized version: Nys-AdaHS. The clas...
Ranking historical players in sports is challenging since some players have never played against each other. It is even more complex in Go because of AlphaGo, a project based on artificial intelligence, who became the world's number 1 after it defeated the 528th and the 4th human Go players. AlphaGo is ranked high in the current Go ranking system b...
In today's large-scaled distributed learning, it often involves a large number of machines. Coordination between them can be very complicated. In order to emphasize the importance of the organic relationships between machines, we introduce the organization theories of human society, such as cooperation and competition, to machine learning. We desig...
Citations
... We argue that time-series prediction models and other machine learning techniques may enhance decision-making in finance. We refer interested readers to recent reference books by Dixon et al. (2020) and Consoli et al. (2021), reviews by West and Bhattacharya (2016) and Henrique et al. (2019), and applications by Moubariki et al. (2019), Li et al. (2021), Kou et al. (2021b) and Manthoulis et al. (2021). ...
... Distance metric learning has aroused significant interest among scholars in machine learning and related fields [1][2][3]. This depends mainly on the intensification of the situation, as follows: Machine learning algorithms always rely on underlying distance metrics that represent important correlations in input data [4][5]. ...
... In addition, in recent years, more and more scholars have studied the closed-loop supply chain from the perspective of green development and sustainability. Gan et al. [19] analyzed the green degree and pricing strategy of centralized and decentralized settings by building a Stackelberg game model for retailers. ...
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... Textual feature analysis involves extracting text information in traffic, such as the URL, packet header, etc., and then using natural language processing technology to preprocess it and convert it into a data format that can be processed by a machine learning model [20]. Li et al. [21] proposed a method combining linear space transformation and nonlinear space transformation. For linear space transformation, it first performs singular value decomposition to obtain the orthogonal space and then uses linear programming to solve the optimal distance metric. ...
... FSL could be considered as deep learning with few labeled samples. Traditional deep neural networks (DNN) usually require a large number of high-quality training samples without bias to avoid overfitting [1][2][3][4][5][6][7][8]. However, due to a number of factors such as privacy, security, or the high cost of labeling data, many real-world application scenarios cannot obtain enough labeled training samples. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/278615930490900-1443438622624_Q64/Gang-Kou-2.jpg)
... Later famous AI examples include Deep Blue-a chess-playing expert system, which defeated chess champion of the time Gary Kasparov in 1997 (19); 20 years later in 2017, Google's AlphaGo, a DL program, defeated the world No. 1 ranked player Jie Ke in a Go match (20); recently in late 2022, OpenAI launched ChatGPT (Chat Generative Pre-trained Transformer), it is a text-generation model that can generate human-like responses based on text input, the model received extensive discussion since its launch (21). These examples used different AI approaches to operate. ...
... • Information technologies: Li et al. (2016) studied the large scale distributed learning systems and showed that most large-scale machine learning applications are running on distributed systems like Hadoop, Spark or Hazel casted and suggested the integration of organic relationships between the cluster nodes. Shon and Moon (2007) introduced a hybrid machine learning approach to network anomaly detection (for instance a cyber-attack) that is more effective compared to non-hybrid ones. ...
