<b>dawai</b>: An<i>R</i>Package for Discriminant Analysis with Additional Information

Conde, David; Fernández, Miguel A.; Salvador, Bonifacio; Rueda, Cristina

doi:10.18637/jss.v066.i10

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The R packages considered for these procedures are as follows. MASS (Venables, 2002) has been used for performing LDA, nnet (Venables, 2002) for performing LOGIT, randomForest (Liaw and Wiener, 2002) for performing RF, e1071 (Meyer et al, 2019) for performing SVM, caTools (Tuszynski, 2019) for performing LGB, dawai (Conde et al, 2015) for performing RLDA, and xgboost (Chen et al, 2019) for performing MONOXGB. To compare the results from the procedures considered we have used several performance criteria.…”

Section: Simulation Studymentioning

confidence: 99%

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Isotonic boosting classification rules

Conde

Fernández

Rueda

et al. 2020

Adv Data Anal Classif

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In many real classification problems a monotone relation between some predictors and the classes may be assumed when higher (or lower) values of those predictors are related to higher levels of the response. In this paper, we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. These algorithms are based on theoretical developments that consider isotonic regression. We show the good performance of these procedures not only on simulations, but also on real data sets coming from two very different contexts, namely cancer diagnostic and failure of induction motors.

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Section: Simulation Studymentioning

confidence: 99%

“…This scheme allows to define rules (Fernández et al, 2006;Conde et al, 2012) that improve the performance of linear discriminant rules. Bootstrap estimators of the performance of these rules have been provided in Conde et al (2013) and the rules have been implemented in the R package dawai presented in Conde et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Isotonic boosting classification rules

Conde

Fernández

Rueda

et al. 2020

Adv Data Anal Classif

Self Cite

View full text Add to dashboard Cite

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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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