Browsing by Author "Lee, Chien-I."

Now showing 1 - 2 of 2

A discretization algorithm based on Class-Attribute Contingency Coefficient
(2008-02-01)
Tsai, Cheng-Jung
;
Lee, Chien-I.
;
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.
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.