Variable selection and error rate estimation in discriminant analysis

Roux, Niël le; Steel, S. J.; Louw, Nelmarie

doi:10.1080/00949659708811856

Cited by 11 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where i denotes the samples from 1 to N. All PLSR models were also validated using a cross model validation routine, which is a two-layer cross-validation and is regarded as more conservative than full cross-validation. 21 In the current study, ten segments of randomly chosen samples were used as default settings. The prediction error of the cross model validated calibration model was expressed as the root mean square error of cross model validation (RMSECMV), resembling the RMSECV.…”

Section: Methodsmentioning

confidence: 99%

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

2006

View full text Add to dashboard Cite

In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.

show abstract

Section: Methodsmentioning

confidence: 99%

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

2006

View full text Add to dashboard Cite

show abstract

“…with H = (I q , I q ) and G = (I q , −I q ), where I q denotes the identity matrix of order q. Performance of the plug-in discriminant functions is usually evaluated by an actual error rate (AER) (Le Roux et al, 1997).…”

Section: Error Rates Of Classificationmentioning

confidence: 99%

Actual Error Rates in Classification of the T-Distributed Random Field Observation Based on Plug-in Linear Discriminant Function

Dučinskas¹,

Zikarienė²

2015

Informatica

View full text Add to dashboard Cite

In current paper a problem of classification of T-distributed random field observation into one of two populations specified by common scaling function is considered. The ML and LS estimators of the mean parameters are plugged into the linear discriminant function. The closed form expressions for the Bayes error rate and the actual error rate associated with the aforementioned discriminant functions are derived. This is the extension of one for the Gaussian case. The actual error rates are used to evaluate and compare the performance of the plug-in discriminant function by means of Monte Carlo study.

show abstract

“…These adaptations can be employed in all-subset selection algorithms for DA, canonical correlation analysis (CCA), or for the description of any``effect'' in MANOVA or MANCOVA models. Celeux [2] and Le Roux et al [16] proposed the direct application of the original leaps bounds algorithm in order to identify the best subsets of each size in two-group DA. Exceptions include: McCabe [18], who adapted Furnival's algorithm to the comparison (according to Wilk's 4) of variable subsets in DA and McHenry [19], who proposed a compromise between stepwise and all-subsets procedures in multivariate linear models.…”

Section: Introductionmentioning

confidence: 99%

“…A possibility in that regard is to use cross-validation techniques that explicitly take into account the selection process (i.e., Snapinn and Knoke [31], Rutter et al [29], Le Roux et al [16]). Although any data-based variable selection procedure usually leads to violations of the assumptions underlying classical inference methods, that should be no reason for ignoring the data in the variable selection process.…”

Section: Introductionmentioning

confidence: 99%

Efficient Variable Screening for Multivariate Analysis

Silva

2001

Journal of Multivariate Analysis

View full text Add to dashboard Cite

It is shown how known algorithms for the comparison of all variables subsets in regression analysis can be adapted to subset comparisons in multivariate analysis, according to any index based on Wilks, Lawley Hotelling, or Bartllet Pillai statistics and, in some special cases, according to any function of the sample squared canonical correlations. The issues regarding the choice of an appropriate comparison criterion are discussed. The computational effort of the proposed algorithms is studied, and it is argued that, for a moderate number of variables, they should be preferred to stepwise selection methods. A software implementation of the methods discussed is freely available and can be downloaded from the Internet. 2000 Academic Press AMS 1991 subject classifications: 62H20; 62H30.

show abstract

Variable selection and error rate estimation in discriminant analysis

Cited by 11 publications

References 8 publications

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

Raman Spectra of Biological Samples: A Study of Preprocessing Methods

Actual Error Rates in Classification of the T-Distributed Random Field Observation Based on Plug-in Linear Discriminant Function

Efficient Variable Screening for Multivariate Analysis

Contact Info

Product

Resources

About