Browsing by Author "Chien, BC"

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A classification tree based on discriminant functions
(2006-05-01)
Chien, BC
;
Lin, JY
;
Yang, WP
;
資訊工程學系
;
Department of Computer Science
The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminant functions which are learned using genetic programming. A discriminant function is a mathematical function for classifying data into a specific class. To learn discriminant functions effectively and efficiently, a distance-based fitness function for genetic programming is designed. After the set of discriminant functions for all classes is generated. a classifier is created as a binary decision tree with the Z-value measure to resolve the problem of ambiguity among discriminant functions. Several popular datasets from the UCI Repository were selected to illustrate the effectiveness of the proposed classifiers by comparing with previous methods. The results show that the proposed classification tree demonstrates high accuracy on the selected datasets.
Comparison and combination of textual and visual features for interactive cross-language image retrieval
(2005)
Cheng, PC
;
Yeh, JY
;
Ke, HR
;
Chien, BC
;
Yang, WP
This paper concentrates on the user-centered search task at ImageCLEF 2004. In this work, we combine both textual and visual features for cross-language image retrieval, and propose two interactive retrieval systems T_ICLEF and VCT_ICLEF. The first one incorporates a relevance feedback mechanism based on textual information while the second one combines textual and image information to help users find a target image. The experimental results show that VCT_ICLEF had a better performance in almost all cases. Overall, it helped users find the topic image within a fewer iterations with a maximum of 2 iterations saved. Our user survey also reported that a combination of textual and visual information is helpful to indicate to the system what a user really wanted in mind.
Learning effective classifiers with Z-value measure based on genetic programming
(2004-10-01)
Chien, BC
;
Lin, JY
;
Yang, WP
;
資訊工程學系
;
Department of Computer Science
This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions effectively, the mechanism called Z-value measure is developed. Based on the Z-value measure, we give two classification algorithms to resolve ambiguity among the discriminant functions. The experiments show that the proposed approach has less training time than previous GP learning methods. The learned classifiers also have high accuracy of classification in comparison with the previous classifiers. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
SMIRE: Similar medical image retrieval engine
(2005)
Cheng, PC
;
Chien, BC
;
Ke, HR
;
Yang, WP
;
資訊工程學系
This paper aims at finding images that are similar to a medical image example query. We propose several image features based on wavelet coefficients, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar medical images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism that clusters visually and textually similar images among these initial results was also proposed to help refine the query. In the ImageCLEF 2004 evaluation, the experimental results show that our system is excellence in mean average precision.