Browsing by Author "Lee, Chien-, I"

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A decision tree-based approach to mining the rules of concept drift
(2007)
Lee, Chien-, I
;
Tsai, Cheng-Jung
;
Wu, Jhe-Hao
;
Yang, Wei-Pang
;
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 top-down and greedy method for discretization of continuous attributes
(2007)
Lee, Chien-, I
;
Tsai, Cheng-Jung
;
Yang, Ya-Ru
;
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.