Browsing by Author "Tsai, Cheng-Jung"

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A decision tree-based approach to mining the rules of concept drift
(2007)
Lee, Chien-, I
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Tsai, Cheng-Jung
;
Wu, Jhe-Hao
;
Yang, Wei-Pang
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資訊工程學系
In a database, the concept of an example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using old dataset is not suitable for predicting new coming dataset. Although many algorithms had been proposed to solve this problem, they focus only on updating the classification model. However in a real life users might be very interested in the rules of concept drift. For example, doctors would desire to know the main causes more for disease variation since such rules would enable them to diagnose patients more correctly and quickly. In this paper we propose a Concept Drift Rule mining Tree to accurately discover the rule of concept drift. The main contributions of this paper are: a) we address the problem of mining concept-drifting rule which was ignored in the past; b) our method can accurately mine the rule of concept drift.
A discretization algorithm based on Class-Attribute Contingency Coefficient
(2008-02-01)
Tsai, Cheng-Jung
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Lee, Chien-I.
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Yang, Wei-Pang
;
資訊工程學系
;
Department of Computer Science
Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. In this paper, we propose a static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification. As to the execution time of discretization, the number of generated rules, and the training time of C5.0, our approach also achieved promising results. (c) 2007 Elsevier Inc. All rights reserved.
A top-down and greedy method for discretization of continuous attributes
(2007)
Lee, Chien-, I
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Tsai, Cheng-Jung
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Yang, Ya-Ru
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Yang, Wei-Pang
;
資訊工程學系
Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier CACC and CAIM achieve comparable results.
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.
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.
Mining decision rules on data streams in the presence of concept drifts
(2009-03-01)
Tsai, Cheng-Jung
;
Lee, Chien-I.
;
Yang, Wei-Pang
;
資訊工程學系
;
Department of Computer Science
In a database, the concept of all example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using the old dataset is not suitable for predicting a new dataset. Therefore, the problem of concept drift has attracted a lot of attention in recent years. Although many algorithms have been proposed to solve this problem, they have not been able to provide users with a satisfactory solution to concept drift. That is, the current research about concept drift focuses only on updating the classification model. However, real life decision makers might be very interested in the rules of concept drift. For example, doctors desire to know the root causes behind variation in the causes and development of disease. In this paper, we propose a concept drift rule mining tree, called CDR-Tree, to accurately discover the underlying rule governing concept drift. The main contributions of this paper are: (a) we address the problem of mining concept-drifting rules which has not been considered in previously developed classification schemes; (b) we develop a method that call accurately mine rules governing concept drift: (c) we develop a method that should classification models be required, call efficiently and accurately generate such models via a simple extraction procedure rather than constructing them anew; and (d) we propose two strategies to reduce the complexity of concept-drifting, rules mined by our CDR-Tree. (C) 2007 Elsevier Ltd. All rights reserved.