A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction
Journal of VLSI Signal Processing Systems
Classification and Localisation of Diabetic-Related Eye Disease
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Small-sample precision of ROC-related estimates
Bioinformatics
Small-sample precision of ROC-related estimates
Bioinformatics
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
IEEE Transactions on Audio, Speech, and Language Processing
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Early detection of squamous dysplasia and esophageal squamous cell carcinoma is of great importance. Adopting computer aided algorithms in predicting cancer risk using its risk factors can serve in limiting the clinical screenings to people with higher risks. In the present study, we show that the application of an advanced classification method, the Minimum Classification Error, could considerably enhance the classification performance in comparison to the logistic regression model and the variable structure fuzzy neural network, as the latest successful methods. The results yield the accuracy of 89.65% for esophageal squamous cell carcinoma, and 88.42% for squamous dysplasia risk prediction.