An Asymptotic Unbiased Technique for Estimating the Error Rates in Discriminant Analysis

Biometrics ◽  
1974 ◽  
Vol 30 (2) ◽  
pp. 239 ◽  
Author(s):  
G. J. McLachlan
Keyword(s):  
Discriminant Analysis ◽  
Error Rates

scholarly journals Evaluation of sensory panels of consumers of specialty coffee beverages using the boosting method in discriminant analysis

2015 ◽  
Vol 36 (6) ◽  
pp. 3671 ◽  
Author(s):  
Gilberto Rodrigues Liska ◽  
Fortunato Silva de Menezes ◽  
Marcelo Angelo Cirillo ◽  
Flávio Meira Borém ◽  
Ricardo Miguel Cortez ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Training Data ◽  
Classification Rule ◽  
Classification Methods ◽  
Training Set ◽  
Linear Discriminant ◽  
Boosting Method

Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher’s Linear Discriminant Analysis (LDA) and discriminant analysis via boosting algorithm (AdaBoost). The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters.

Estimation of Error Rates in Several-Population Discriminant Analysis

1982 ◽  
Vol 19 (1) ◽  
pp. 57-61 ◽  
Author(s):  
Stephen C. Hora ◽  
James B. Wilcox
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Posterior Probability ◽  
Error Rates ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Probability Estimator ◽  
Estimation Of Error

Researchers seeking to estimate the classification accuracy of linear discriminant functions in a more than two-population setting have had little guidance as to the most appropriate technique. The authors review the available techniques and present an additional alternative which combines features of the U-method and the recently developed posterior probability estimator. The new alternative is compared with other methods by Monté Carlo simulation.

scholarly journals Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3364
Author(s):  
Ming-Jer Jeng ◽  
Mukta Sharma ◽  
Lokesh Sharma ◽  
Shiang-Fu Huang ◽  
Liann-Be Chang ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Discriminant Analysis ◽  
Oral Cancer ◽  
Raman Spectra ◽  
Cross Validation ◽  
Error Rates ◽  
Control Group ◽  
Optical Techniques ◽  
Linear Discriminant

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

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