Browsing by Author "Lee, Chien-I"

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An approach to mining the multi-relational imbalanced database
(2008-05-04)
Lee, Chien-I
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Tsai, Cheng-Jung
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Wu, Tong-Qin
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Yang, Wei-Pang
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資訊工程學系
The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database. (c) 2007 Elsevier Ltd. All rights reserved.
An efficient and sensitive decision tree approach to mining concept-drifting data streams
(2008)
Tsai, Cheng-Jurig
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Lee, Chien-I
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Yang, Wei-Pang
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資訊工程學系
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Department of Computer Science
Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X(2) test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required.
Detecting Drifting Concepts on the Internet
(2008)
Lee, Chien-I
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Tsai, Cheng-Jung
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Hsieh, Chien-Hui
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資訊工程學系
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Department of Computer Science
With the explosive growth of information sources available on the World Wide Web, it has become increasingly necessary to utilize automated tools to discovery interesting and potentially useful patterns from data on the Internet. Since the data on the Internet such as communication packages, email, and e-commerce transactions come consecutively, an efficient and accurate incremental learning approach is required. Moreover, since the labels of these data may change over time, the problem of concept drift must be considered while incrementally learning from the data on the Internet. In this paper, we give a detailed discussion of the concept-drifting problem on the Internet. We also address a new problem called two-way drift. An approach adapted to the occurrence of concept drift is then proposed as the solution to incrementally learn from the data on the Internet. Our approach works as a preprocessor to detect the occurrence of concept drift and can be incorporated into any existing classification techniques. Our approach can also reveal which attribute values cause concept drift and therefore enables systems or decision makers to adopt proper decision in advance.