IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Guide To The Project Management Body Of Knowledge (PMBOK Guides)
A Guide To The Project Management Body Of Knowledge (PMBOK Guides)
Software project effort estimation with voting rules
Decision Support Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A support vector regression based prediction model of affective responses for product form design
Computers and Industrial Engineering
Computers and Industrial Engineering
Using neural networks to detect the bivariate process variance shifts pattern
Computers and Industrial Engineering
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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The thin-film transistor liquid-crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively.