Robustness of classification rules that incorporate additional information

Salvador, Bonifacio; Fernández, Miguel A.; Martı́n, I.; Rueda, Cristina

doi:10.1016/j.csda.2007.08.017

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In a series of papers, the rules appearing in that initial paper have been improved, first to deal with more general types of information expressed in terms of cones of restrictions and general covariance matrices in Fernández et al (2006) and later to the case of more than two populations in Conde et al (2012). The robustness of the rules has also been studied in Salvador, Fernández, Martín, and Rueda (2008) and good estimators of the performance of the rules (which is an essential issue in discriminant analysis) have been provided in Conde et al (2013). From now on, we will refer to these rules as restricted rules as the additional information is incorporated through restrictions on the population means.…”

Section: Introductionmentioning

confidence: 99%

dawai: AnRPackage for Discriminant Analysis with Additional Information

Conde¹,

Fernández²,

Salvador³

et al. 2015

J. Stat. Soft.

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The incorporation of additional information into discriminant rules is receiving increasing attention as the rules including this information perform better than the usual rules. In this paper we introduce an R package called dawai, which provides the functions that allow to define the rules that take into account this additional information expressed in terms of restrictions on the means, to classify the samples and to evaluate the accuracy of the results. Moreover, in this paper we extend the results and definitions given in previous papers (Fernández,

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Section: Introductionmentioning

confidence: 99%

dawai: AnRPackage for Discriminant Analysis with Additional Information

Conde¹,

Fernández²,

Salvador³

et al. 2015

J. Stat. Soft.

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show abstract

“…One of them is the robustness of the rule with respect to its theoretical assumptions. The robustness properties of the rules that incorporate additional information have been studied in Salvador et al (2008). Another issue, at least as important as the robustness of a rule, is the proper evaluation of the error of the rule in practice.…”

Section: Introductionmentioning

confidence: 99%

Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial

Conde

Salvador

Rueda

et al. 2013

Statistical Applications in Genetics and Molecular Biology

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Classification rules that incorporate additional information usually present in discrimination problems are receiving certain attention during the last years as they perform better than the usual rules in poor discrimination problems. Fernández et al (2006) proved that these rules have a lower unconditional misclassification probability than the usual Fisher's rule but they did not consider the estimation of the conditional error probability when a training sample is given (the so-called true error rate) which is a very interesting parameter in practice.In this paper we consider the problem of estimating the true error rate of these classification rules in the classical topic of discrimination among two normal populations. We prove theoretical results on the apparent error rate of the rules that expose the need of new estimators of their true error rate. Our proposal is to also consider the additional information in the definition of the true error rate estimators. We propose four such new estimators. Two of them are defined incorporating the additional information into the leave-one-out-bootstrap. The other two are the corresponding cross-validation after bootstrap versions. We compare these new estimators with the usual ones in a simulation study and in a cancer trial application, showing the very good behavior, in terms of mean square error, of the leave-one-out bootstrap estimators that incorporate the available additional information.

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Classification of Samples with Order-Restricted Discriminant Rules

Conde

Fernández

Salvador

et al. 2016

Methods in Molecular Biology

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In recent years, mass spectrometry techniques have helped proteomics to become a powerful tool for the early diagnosis of cancer, as they help to discover protein profiles specific to each pathological state. One of the questions where proteomics is giving useful practical results is that of classifying patients into one of the possible severity levels of an illness, based on some features measured on the patient. This classification is usually made using one of the many discrimination procedures available in statistical literature. We present in this chapter recently developed restricted discriminant rules that use additional information in terms of orderings on the means, and we illustrate how to apply them to mass spectrometry data using R package dawai. Specifically, we use proteomic prostate cancer data, and we describe all steps needed, including data preprocessing and feature extraction, to build a discriminant rule that classifies samples in one of several disease stages, thus helping diagnosis. The restricted discriminant rules are compared with some standard classifiers that do not take into account the additional information, showing better performance in terms of error rates.

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Robustness of classification rules that incorporate additional information

Cited by 3 publications

References 10 publications

dawai: AnRPackage for Discriminant Analysis with Additional Information

dawai: AnRPackage for Discriminant Analysis with Additional Information

Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial

Classification of Samples with Order-Restricted Discriminant Rules

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