Classification and Error Estimation for Discrete Data

Braga-Neto, Ulisses

doi:10.2174/138920209789208228

Cited by 12 publications

(11 citation statements)

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“…This method provides a measure of the network's learning ability; yet it is not preferable for performance evaluation tasks as it is known to be optimistically biased. However, as shown in [ 52 , 53 ], in discrete classification problems with large-sample categorical datasets, like the classification problem of this study, resubstitution can be significantly accurate relative to more complex error estimation schemes, since the optimistic bias and the variance of the method tend to be vanished as the sample size increases, provided that classifier complexity is not too high. For this reason we decided to take into consideration the performance of the final models when for testing the training and the validation sets are used.…”

Section: Methodsmentioning

confidence: 90%

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Bountris

Haritou

Pouliakis

et al. 2014

BioMed Research International

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Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

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Section: Methodsmentioning

confidence: 90%

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Bountris

Haritou

Pouliakis

et al. 2014

BioMed Research International

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“…In analogous fashion to the performance metrics of prediction error estimators [8], the key performance metrics for an CoD estimator CoD are its bias…”

Section: Performance Metrics Of Cod Estimatorsmentioning

confidence: 99%

“…The error of the best predictor corresponds to the optimal prediction error, also known as Bayes error, which depends only on the underlying probability model [7]. However, in practical real-world problems, the underlying probability model is unknown, and thus we arrive at the fundamental issue of how to find a good prediction error estimator in small-sample settings [8,9]. An error estimator may be a deterministic function of the sample data, in which case it is called a nonrandomized error estimator; such popular error estimators as resubstitution and leave-oneout are examples.…”

Section: Introductionmentioning

confidence: 99%

“…The CoD with the true values of ε and ε 0 in (1) will be called in this paper the "actual CoD." We will employ the discrete histogram rule [7,8], the most widely used and intuitive rule for discrete prediction problems, in order to estimate prediction errors and CoDs from the sample data. This paper presents, for the first time, an exact formulation for performance metrics of the resubstitution and leaveone-out CoD estimators, for the discrete histogram rule.…”

Section: Introductionmentioning

confidence: 99%

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Exact Performance of CoD Estimators in Discrete Prediction

Chen

Braga-Neto

2010

EURASIP J. Adv. Signal Process.

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The coefficient of determination (CoD) has significant applications in genomics, for example, in the inference of gene regulatory networks. We study several CoD estimators, based upon the resubstitution, leave-one-out, cross-validation, and bootstrap error estimators. We present an exact formulation of performance metrics for the resubstitution and leave-one-out CoD estimators, assuming the discrete histogram rule. Numerical experiments are carried out using a parametric Zipf model, where we compute exact performance metrics of resubstitution and leave-one-out CoD estimators using the previously derived equations, for varying actual CoD, sample size, and bin size. These results are compared to approximate performance metrics of 10-repeated 2-fold cross-validation and 0.632 bootstrap CoD estimators, computed via Monte Carlo sampling. The numerical results lead to a perhaps surprising conclusion: under the Zipf model under consideration, and for moderate and large values of the actual CoD, the resubstitution CoD estimator is the least biased and least variable among all CoD estimators, especially at small number of predictors. We also observed that the leave-one-out and cross-validation CoD estimators tend to perform the worst, whereas the performance of the bootstrap CoD estimator is intermediary, despite its high computational complexity.

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“…Therefore, classifier design and error estimation of the designed classifiers become key factors to perform such a process successfully. From these two tasks, error estimation is perhaps the most critical task since the error estimator needs to preserve the true ordering [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Cattle Coat Color Based on Genotype Using Pattern Recognition Methods

et al. 2015

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Abstract-Several current research projects are focused on the creation of haplotype maps to identify and describe common genetic variation in some species. Studies on haplotype maps are key in understanding how natural selection has produced genomic differences between subspecies of a given species. Important insight can be obtained by determining which variations in the genotype are associated with important phenotypical differences between individuals. Pattern recognition theory and machine learning techniques are useful tools to reveal this connection from a large amount of data provided by haplotype maps. In this work, we applied discrete classifiers and feature selection techniques for the prediction of cattle coat color from genotypes. We compared the performance of different classification rules and showed the feasibility of this approach for the prediction of phenotype based on genotype.

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Classification and Error Estimation for Discrete Data

Cited by 12 publications

References 50 publications

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Exact Performance of CoD Estimators in Discrete Prediction

Classification of Cattle Coat Color Based on Genotype Using Pattern Recognition Methods

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