ライフサイエンスコーパス: discriminant analysis

1 icornavirus (P < .0001; partial least square discriminant analysis).

2 e fingerprints (Q(2) of 0.784 for supervised discriminant analysis).

3 ear support vector machine and kernel Fisher discriminant analysis.

4 samples were correctly classified by linear discriminant analysis.

5 cluding the t-test and partial least squares discriminant analysis.

6 ogistic regression and partial least squares discriminant analysis.

7 e statistical technique Partial Least Square-Discriminant Analysis.

8 antly associated with GBS carriage by linear discriminant analysis.

9 tatistical technique of partial least square discriminant analysis.

10 ividual indices were built using linear step discriminant analysis.

11 -test, fold changes and partial least square discriminant analysis.

12 r example 0.2 to 13% improvement over linear discriminant analysis.

13 entional statistics and partial least square discriminant analysis.

14 e results, further analysed through a linear discriminant analysis.

15 sured observables following two class linear discriminant analysis.

16 was the best predictor of reentry in linear discriminant analysis.

17 erve as keystone taxa as revealed our linear discriminant analysis.

18 ns to latent structures (OPLS) and OPLS with discriminant analysis].

19 Using Forward Stepwise Discriminant Analysis, 3 statistical models were created

20 cation of origin (Karoo vs. Non-Karoo) using discriminant analysis allowed 95% and 90% correct classi

21 Furthermore, linear discriminant analysis allowed achieving sensitivities gr

22 We sought to determine whether discriminant analysis allows prognostication in patients

23 Partial least squares discriminant analysis also allowed prediction of the deg

24 e plots and orthogonal partial least squares discriminant analysis also showed significant difference

25 Partial least squares-discriminant analysis also showed that these markers wer

26 ponent analysis (PCA), partial least squares-discriminant analysis, analysis of variance, and random

27 ction of food frauds were analyzed employing discriminant analysis and a one-class classifier.

28 Partial Least Squares regression, Discriminant Analysis and Artificial Neural Networks wer

29 Multivariate classification comprises discriminant analysis and class-modeling techniques wher

30 cipal component analysis coupled with linear discriminant analysis and classification models demonstr

31 ombination of principal component and linear discriminant analysis and least absolute shrinkage and s

32 ted by linear Support Vector Machine, Linear Discriminant Analysis and Naive Bayesian classifiers acr

33 cipal component analysis coupled with linear discriminant analysis and predictive classifiers were ap

34 alysis, linear discriminant analysis, binary discriminant analysis and random forest.

35 Discriminant analysis and SVM were used to classify new

36 metric techniques like partial least squares discriminant analysis and variable identification (PLS-D

37 lysis, such as PLS-DA (Partial Least Squares Discriminant Analysis) and LDA (Linear Discriminant Anal

38 ysis (PARAFAC), PARAFAC supervised by linear discriminant analysis, and discriminant unfolded partial

39 Partial least squared (PLS)-discriminant analysis, and PLS-regression models were as

40 significance testing, partial least squares discriminant analysis, and receiver operating characteri

41 were analyzed by using partial least-squares discriminant analysis, and the results were validated wi

42 s based on principal components analysis and discriminant analysis applied to the volatile profile of

43 A simplified discriminant analysis based on 3 common clinical variabl

44 On the other hand, linear discriminant analysis based on 8 selected absorbance val

45 discrimination models were created by linear discriminant analysis based on principal component analy

46 The discriminant analysis based on several anthocyanins, org

47 Simplified discriminant analysis based on unsupervised clustering h

48 The discriminant analysis, based on the use of one input-cla

49 Including all studied regions and using discriminant analysis between PSP and PD, 100% sensitivi

50 hine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and

51 Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares).

52 The study confirms that discriminant analysis can be successful in distinguishin

53 ombination of dimension reduction and linear discriminant analysis, CARS (AUC = 0.93) and TPEF (AUC =

54 oss-validation was employed to test a Linear Discriminant Analysis classifier based upon the RT-qPCR-

55 tor variables were derived to train a linear discriminant analysis classifier by using a leave-one-ou

56 Partial least square discriminant analysis clearly separated IC patients from

57 A discriminant analysis correctly classified 96.7% of the

58 thin palmitate and total carotenoid content, discriminant analysis correctly classified all samples a

59 ication models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection

60 Linear Discriminant Analysis, coupled with Successive Projectio

61 Results demonstrated that Discriminant Analysis (DA) and Correlated Component Regr

62 ndent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra

63 Discriminant Analysis (DA) led to similar (conventional/

64 Discriminant Analysis (DA) was successful for botanical

65 n harvesting years was studied by performing discriminant analysis (DA), k nearest neighbours (kappa-

66 density, were processed using multivariate, discriminant analysis (DA), principal component analysis

67 the year, season and production region using discriminant analysis (DA).

68 Multivariate partial least squares discriminant analysis demonstrated similar bacterial pro

69 ics, we have developed partial least-squares-discriminant analysis derived decision algorithms that o

70 Yet, discriminant analysis did not adequately distinguish the

71 Linear discriminant analysis distinguished facial change determ

72 uation logistic regression models and linear discriminant analysis effect size (LEfSe) algorithm to i

73 nt of CDI were identified by means of linear discriminant analysis effect size analysis and then furt

74 d algorithm, expectation-maximization sparse discriminant analysis (EM-SDA), produces a sparse LDA mo

75 Fisher Discriminant Analysis enables multivariate classificatio

76 The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is

77 e developed a novel method based on Fisher's Discriminant Analysis (FDA) to identify gene expression

78 ror, 2.03-12.48%) were acquired by nonlinear discriminant analysis for CRC, IBD and NTC, regardless o

79 lysis using orthogonal partial least squares discriminant analysis for metabolomics.

80 Recently, Witten proposed a Poisson linear discriminant analysis for RNA-Seq data.

81 paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data.

82 SOM-RPM can be further analyzed using linear discriminant analysis for the validation and characteriz

83 ed, via thermophoretic enrichment and linear discriminant analysis, for cancer detection and classifi

84 spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnos

85 results obtained by Genetic Algorithm Linear Discriminant Analysis (GA-LDA) were the most satisfactor

86 ined using the genetic algorithm with linear discriminant analysis (GA-LDA).

87 etic algorithms-partial least-squares-linear discriminant analysis (GA-PLS-LDA).

88 Three of 5 clusters identified by means of discriminant analysis had improved SNOT-22 outcomes with

89 Partial least squares discriminant analysis identified a signature of 54 basel

90 From discriminant analysis in 103 patients with full clinical

91 rometry performed, with partial least square discriminant analysis integrating clinical, microbiome,

92 nsform micro-Raman spectroscopy coupled with Discriminant Analysis is here presented.

93 hine classification and Linear and Quadratic Discriminant Analysis (LDA and QDA, respectively).

94 ng n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based class

95 and class modelling methods by using linear discriminant analysis (LDA) and soft independent model c

96 The authentication issue was faced by Linear Discriminant Analysis (LDA) and Soft Independent Modelli

97 nd computational approaches including linear discriminant analysis (LDA) and sparse canonical correla

98 amples was most successful when using linear discriminant analysis (LDA) and taking into account the

99 s an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning met

100 xtracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their

101 Moreover, using Linear Discriminant Analysis (LDA) coupled with effect size mea

102 In addition, Linear discriminant analysis (LDA) effect size (LEfSe) method r

103 rincipal component analysis (PCA) and linear discriminant analysis (LDA) for the differentiation of p

104 In this study, Linear Discriminant Analysis (LDA) is applied to investigate th

105 rincipal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authenti

106 mong the multivariate analysis tools, linear discriminant analysis (LDA) of fruit multi elemental fin

107 rincipal component analysis (PCA) and linear discriminant analysis (LDA) showed better results consid

108 Linear discriminant analysis (LDA) successfully recognizes the

109 of FT-NIR spectroscopy combined with Linear Discriminant Analysis (LDA) to discriminate chickpea see

110 ential forward selection coupled with linear discriminant analysis (LDA) was the best strategy to sel

111 A machine-learning algorithm called linear discriminant analysis (LDA) was trained by using the lar

112 Raman spectroscopy with linear discriminant analysis (LDA) was used to identify changes

113 rincipal component analysis (PCA) and linear discriminant analysis (LDA) were performed.

114 tern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by s

115 type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cyto

116 rincipal component analysis (PCA) and linear discriminant analysis (LDA), an identification model pro

117 ysis (PARAFAC), PARAFAC supervised by linear discriminant analysis (LDA), and discriminant unfolded p

118 Principal component analysis (PCA), linear discriminant analysis (LDA), k-nearest neighbors (kNN),

119 attern recognition techniques such as linear discriminant analysis (LDA), partial least square discri

120 d with discriminant analysis, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Anal

121 In this paper, Linear Discriminant Analysis (LDA), Quadratic Discriminant Anal

122 , principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SV

123 rincipal component analysis (PCA) and linear discriminant analysis (LDA), were used to differentiate

124 ochastic neighbor embedding (tSNE) or linear discriminant analysis (LDA).

125 y classification models, specifically linear discriminant analysis (LDA).

126 omponent analysis (PCA) followed by a linear discriminant analysis (LDA).

127 component analysis (PCA) followed by linear discriminant analysis (LDA).

128 rincipal component analysis (PCA) and linear discriminant analysis (LDA).

129 of ecological character states using linear discriminant analysis (LDA).

130 rinciple component analysis (PCA) and linear discriminant analysis (LDA).

131 modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, bin

132 -weighted multiblock partial least squares - discriminant analysis (MB-PLS1-DA) models were compared

133 A discriminant analysis method was employed in the classif

134 Fisher's linear discriminant analysis method was employed to classify aw

135 ed training set, (18)F-FDG PET with advanced discriminant analysis methods is able to accurately dist

136 d anatomy ontology terms, employing Fisher's Discriminant Analysis methods to identify previously unk

137 e features were used as input to a quadratic discriminant analysis ML classifier, which was trained,

138 A Linear Discriminant Analysis model based on the abundance of 12

139 on of the groups in the partial least square-discriminant analysis model was based, demonstrated the

140 The LDA (linear discriminant analysis) model showed that the FAMEs conte

141 Partial Least Squares-Discriminant Analysis modelling of fused PCA scores of t

142 ak volume ratios were used to develop linear discriminant analysis models able to distinguish olive l

143 r data exploration and partial least squares discriminant analysis models for the differences between

144 Three partial least square-discriminant analysis models were developed in order to

145 Several Orthogonal Partial Least Squares-Discriminant Analysis models were generated from the acq

146 urements obtained from partial least-squares discriminant analysis models.

147 ed diagnostic groups (multiblock barycentric discriminant analysis [MUBADA]) was used.

148 applying the tested Partial Least Squares - Discriminant Analysis multiclass model (85 fillets) to t

149 a(13)C values, apatite-collagen spacing, and discriminant analysis of delta(13)C(coll), delta(13)C(ca

150 The Discriminant Analysis of MultiAspect CYtometry (DAMACY)

151 Discriminant analysis of principal components (DAPC) rev

152 = 0.65), Bayesian analysis in STRUCTURE and discriminant analysis of principal components (DAPC) sho

153 also reflected in both network analyses and discriminant analysis of principal components.

154 to decide which method should be used in the discriminant analysis of RNA-Seq data.

155 Partial least squares-discriminant analysis of the MS/MS(ALL) lipidomic datase

156 Linear discriminant analysis on the physicochemical parameters

157 lling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabolomic data read

158 An orthogonal partial least squares discriminant analysis (OPLS-DA) score plot revealed good

159 Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) showed clear group disti

160 ) followed by orthogonal partial last square-discriminant analysis (OPLS-DA) to develop classifiers a

161 Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully

162 Afterwards, orthogonal partial least square discriminant analysis (OPLS-DA) was favorably used to di

163 s (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to characte

164 analysed by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) which revealed a clear d

165 ysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hierarchical cluste

166 Using orthogonal partial last-squares discriminant analysis (OPLS-DA), multivariate models wer

167 d Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA).

168 as well as Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).

169 s (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

170 ysis (PLS-DA) and Principal Component-Linear Discriminant Analysis (PC-LDA) classification models wer

171 Principal components linear discriminant analysis (PC-LDA) of acquired RS-spectra co

172 nd multivariate principal component analysis-discriminant analysis (PCA-DA) statistics applied to the

173 ncipal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Square

174 ncipal component analysis followed by linear discriminant analysis (PCA-LDA) was used for the multiva

175 ns using principal component analysis linear discriminant analysis (PCA-LDA).

176 and principal component analysis with linear discriminant analysis (PCA-LDA).

177 region of 4000-5000 cm(-1), piecewise linear discriminant analysis (PLDA) is used to classify spectra

178 sion 7.2 software) and partial least squares-discriminant analysis (PLS-DA) (Matlab R2017b) were used

179 Furthermore, the partial least square discriminant analysis (PLS-DA) achieved an effective cla

180 In addition, partial least square discriminant analysis (PLS-DA) achieved an effective cla

181 On the basis of the partial least-squares-discriminant analysis (PLS-DA) and ANOVA-simultaneous co

182 ur algorithm, based on partial least squares-discriminant analysis (PLS-DA) and cross-validation by b

183 ierarchical clustering, Partial-least square discriminant analysis (PLS-DA) and Ingenuity pathway ana

184 odels were developed by Partial Least Square-Discriminant Analysis (PLS-DA) and internally validated

185 opy in combination with Partial Least Square Discriminant Analysis (PLS-DA) and Partial Least Square

186 py, and analysed using partial least squares discriminant analysis (PLS-DA) and partial least squares

187 Partial-Least Squares-Discriminant Analysis (PLS-DA) and Principal Component-L

188 Partial least squares discriminant analysis (PLS-DA) and soft independent mode

189 Partial least squares discriminant analysis (PLS-DA) and soft independent mode

190 earest neighbors (kNN), partial least square-discriminant analysis (PLS-DA) and support vector machin

191 (MIR) spectroscopy and partial least squares discriminant analysis (PLS-DA) as a means to discriminat

192 Among the samples, partial linear square discriminant analysis (PLS-DA) classified 50.2% of the s

193 rediction errors using partial least squares discriminant analysis (PLS-DA) discrimination models for

194 s (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) from liquid chromatograph

195 Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to d

196 Partial least-squares discriminant analysis (PLS-DA) indicated that both metho

197 Partial least square-discriminant analysis (PLS-DA) indicated that seeds from

198 Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine lear

199 The partial least-squares discriminant analysis (PLS-DA) model built to discrimina

200 Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and

201 Four different partial least squares discriminant analysis (PLS-DA) models were fitted to the

202 bolomics combined with partial least squares-discriminant analysis (PLS-DA) multivariate analysis rev

203 recognition analysis, partial-least-squares discriminant analysis (PLS-DA) of images, to develop a r

204 Partial Least Squares-Discriminant Analysis (PLS-DA) of metabolomes of individ

205 Partial least squares-linear discriminant analysis (PLS-DA) provided a 70% success ra

206 fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be th

207 Partial least squares discriminant analysis (PLS-DA) showed clear discriminati

208 Using partial least-squares discriminant analysis (PLS-DA) to compare results betwee

209 troscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) to discriminate the origi

210 overall structure, and partial least square discriminant analysis (PLS-DA) was carried out for the a

211 Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect f

212 Partial least squares discriminant analysis (PLS-DA) was used to determine spe

213 Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR d

214 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investig

215 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were evaluated.

216 Partial least-squared discriminant analysis (PLS-DA) with double leave-one-pat

217 Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS we

218 ectra was subjected to partial least squares-discriminant analysis (PLS-DA), a multivariate statistic

219 ts were obtained for partial least squares - discriminant analysis (PLS-DA), allowing a good discrimi

220 Using partial least squares discriminant analysis (PLS-DA), it was possible to diffe

221 iminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-N

222 ecognition techniques, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modelin

223 rest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine c

224 ent analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the purees may be alloca

225 ogistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explor

226 ed classification with Partial Least Squares Discriminant Analysis (PLS-DA).

227 fied and submitted to a Partial-Least Square Discriminant Analysis (PLS-DA).

228 copy combined with partial least squares for discriminant analysis (PLS-DA).

229 nt analysis (PCA), and partial least squares-discriminant analysis (PLS-DA).

230 ass analogy (SIMCA) and partial least square discriminant analysis (PLS-DA).

231 ic algorithm (GA); and partial least squares discriminant analysis (PLS-DA).

232 s combined, the partial least squares-linear discriminant analysis (PLS-LDA) model resulted in 75% an

233 tric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied

234 ent analysis, PCA, and partial least squares discriminant analysis, PLS-DA) were applied to deconvolu

235 frared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach.

236 was obtained using the partial least squares discriminant analysis (PLSDA) algorithm.

237 sification models with partial least-squares discriminant analysis (PLSDA) and obtaining average stoc

238 least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN)

239 ent analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS

240 The output of the linear discriminant analysis provided a qEEV-based response ind

241 turbations [orthogonal partial least-squares discriminant analysis Q(2)(Y) of 0.728] in the fecal lip

242 inear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines

243 inear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines

244 ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB.

245 VOI-based discriminant analysis resulted in an 88.8% accuracy in p

246 omparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar patt

247 Partial least square-discriminant analysis revealed metabolite profiles that

248 Linear Discriminant Analysis revealed significant clustering ba

249 Discriminant analysis revealed that for each grape culti

250 Linear discriminant analysis reveals shape features that specif

251 nearest shrunken centroids (NSCs) and sparse discriminant analysis (SDA) with k-nearest neighbors for

252 Discriminant analysis showed delta(13)C and delta(15)N d

253 Partial least squares-discriminant analysis showed that the 30 most important

254 Stepwise Linear Discriminant Analysis (SLDA) showed that a reduced numbe

255 ve projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all

256 Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classif

257 regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS-DA) and support vector class

258 system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector mach

259 was achieved by super partial least-squares discriminant analysis (sPLS-DA), support vector machine

260 PERMANOVA and sparse partial least squares discriminant analysis (sPLSDA) demonstrated that subject

261 feature selection algorithms associated with discriminant analysis, such as Linear Discriminant Analy

262 Support vector machines-discriminant analysis (SVM-DA) was used for differentiat

263 neighbors (KNN), and support vector machines discriminant analysis (SVMDA).

264 Further, a VOI derived by discriminant analysis that maximally separated diagnosti

265 In discriminant analysis, these baseline factors identified

266 nalysis (principal component analysis-linear discriminant analysis), this method was successfully app

267 noids were considered as variables in linear discriminant analysis to attempt geographical classifica

268 orthogonal) projections to latent structures-discriminant analysis to characterise and differentiate

269 lected by gas chromatography associated with discriminant analysis to differentiate milk and whey, as

270 itudinal abundance of metabolites; 4) linear discriminant analysis to evaluate robustness in a second

271 e developed a novel method based on Fisher's Discriminant Analysis to identify gene expression weight

272 e conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discr

273 The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extr

274 The application of linear discriminant analysis to our overall results demonstrate

275 uares Discriminant Analysis) and LDA (Linear Discriminant Analysis), to implement a traceability syst

276 Moreover, discriminant analysis using ASD subdomains distinguished

277 o 5 clusters based on a previously described discriminant analysis using total Sino-Nasal Outcome Tes

278 stems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared s

279 Models were built using linear discriminant analysis via the misclassification penalize

280 dministration and the multivariate canonical discriminant analysis was able to distinguish between tr

281 Partial least squares discriminant analysis was applied to the spectral datase

282 The linear discriminant analysis was conducted in order to classify

283 Then partial least-squares-discriminant analysis was conducted to investigate the b

284 Discriminant analysis was extended to the evaluation of

285 Orthogonal partial least squares-discriminant analysis was performed to select features c

286 an exploratory assessment relying on Linear Discriminant Analysis was performed.

287 Discriminant analysis was successfully discriminated the

288 Orthogonal partial least squares discriminant analysis was used to build models separatin

289 Principal component fed linear discriminant analysis was used to develop a classificati

290 f criteria for AMR and partial least squares discriminant analysis was used to identify associated ch

291 IR in combination with Partial Least Squares-Discriminant Analysis we were able to discriminate infec

292 ntensity variations, principal component and discriminant analysis were performed to discriminate the

293 ipal component analysis, factor analysis and discriminant analysis were used for the statistical eval

294 parameters, as also confirmed in the linear discriminant analysis, where these parameters were not s

295 ical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the

296 The aging type could be defined by linear discriminant analysis with an accuracy of 95%.

297 differentiation models were built by linear discriminant analysis with the percentage of correct cla

298 The best results were obtained using Discriminant Analysis, with 95% correct re-classificatio

299 Extreme gradient boosting discriminant analysis (XGBDA) was examined among other m

300 Using linear discriminant analysis, XRF-based multi-elements with and