C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Classification of imbalanced remote-sensing data by neural networks
Pattern Recognition Letters - special issue on pattern recognition in practice V
Data mining: concepts and techniques
Data mining: concepts and techniques
Robust Classification for Imprecise Environments
Machine Learning
Feature selection with neural networks
Pattern Recognition Letters
Machine Learning
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Comparisons of Classification Methods for Screening Potential Compounds
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Tutorial: Trends in personal wireless data communications
Computer Communications
An Evaluation of the Robustness of MTS for Imbalanced Data
IEEE Transactions on Knowledge and Data Engineering
An information granulation based data mining approach for classifying imbalanced data
Information Sciences: an International Journal
MDS: a novel method for class imbalance learning
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
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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.