Browsing by Author "Chen, LS"

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A neural network based information granulation approach to shorten the cellular phone test process
(2006-06-01)
Su, CT
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Chen, LS
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Chiang, TL
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工業工程與管理學系
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Department of Industrial Engineering and Management
In the cellular phone OEM/ODM industry, reducing test time and cost are crucial due to fierce competition, short product life cycle, and a low margin environment. Among the inspection processes, the radio frequency (RF) function test process requires more operation time than any other. Hence, manufacturers need an effective method to reduce the RF test items so that the inspection time can be reduced while maintaining the quality of the RF function test. However, traditional feature selection methods such as neural networks and genetic algorithm lead to a high level of Type II error in the situation of imbalanced data where the amount of good products is far greater than the defective products. In this study, we propose a neural network based information granulation approach to reduce the RF test items for the finished goods inspection process of a cellular phone. Implementation results show that the RF test items were significantly reduced, and that the inspection accuracy remains very close to that of the original testing process. In addition, the Type II errors decreased as well. (C) 2006 Elsevier B.V. All rights reserved.
Knowledge acquisition through information granulation for imbalanced data
(2006-10-01)
Su, CT
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Chen, LS
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Yih, YW
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工業工程與管理學系
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Department of Industrial Engineering and Management
When learning from imbalanced/skewed data, which almost all the instances are labeled as one class while far few instances are labeled as the other class, traditional machine learning algorithms tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class. This paper proposes a novel method called 'knowledge acquisition via information granulation' (KAIG) model which not only can remove some unnecessary details and provide a better insight into the essence of data but also effectively solve 'class imbalance' problems. In this model, the homogeneity index (H-index) and the undistinguishable ratio (U-ratio) are successfully introduced to determine a suitable level of granularity. We also developed the concept of sub-attributes to describe granules and tackle the overlapping among granules. Seven data sets from UCI data bank, including one imbalanced diagnosis data (pima-Indians-diabetes), are provided to evaluate the effectiveness of KAIG model. By using different performance indexes, overall accuracy, G-mean and Receiver Operation Characteristic (ROC) curve, the experimental results comparing with C4.5 and Support Vector Machine (SVM) demonstrate the superiority of our method. (c) 2005 Elsevier Ltd. All rights reserved.
Using hypermedia in computer-aided instruction
(1996-05-01)
Chen, LS
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Liu, PW
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Chang, KY
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Chen, JP
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Chen, SC