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Classification and regression tree

Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J., 1984. Classification and Regression Trees. Wadsworth and Brooks. [Pg.301]

Breiman, L., et al., Classification and Regression Trees. Wadsworth, Belmont, CA, 1984. Clausing, D., Total Quality Development. ASME Press, New York, 1993. [Pg.154]

Deconinck, E Hancock, T Coomans, D., Massart, D. L, Vander Heyden, Y. Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. [Pg.107]

Additionally, Breiman et al. [23] developed a methodology known as classification and regression trees (CART), in which the data set is split repeatedly and a binary tree is grown. The way the tree is built, leads to the selection of boundaries parallel to certain variable axes. With highly correlated data, this is not necessarily the best solution and non-linear methods or methods based on latent variables have been proposed to perform the splitting. A combination between PLS (as a feature reduction method — see Sections 33.2.8 and 33.3) and CART was described by... [Pg.227]

C.H. Yeh and C.H. Spiegelman, Partial least squares and classification and regression trees. Chemom. and Intell. Lab. Systems, 22 (1994) 17-23. [Pg.240]

Classification and regression trees Input partition Adaptive shape, piecewise constant [/3, (], minimum output prediction error... [Pg.34]

Garzotto M, Beer TM, Hudson RG et al. Improved detection of prostate cancer using classification and regression tree analysis. JCh n Oncol 2005 23 4322 329. [Pg.369]

Canonical Correlation Analysis Principal Component Regressionb Classification and Regression Trees (CART) Linear Learning Machine Neural Networks Adaptive Least Squares Genetic Programming Logistic Regression... [Pg.168]

Examples of mathematical methods include nominal range sensitivity analysis (Cullen Frey, 1999) and differential sensitivity analysis (Hwang et al., 1997 Isukapalli et al., 2000). Examples of statistical sensitivity analysis methods include sample (Pearson) and rank (Spearman) correlation analysis (Edwards, 1976), sample and rank regression analysis (Iman Conover, 1979), analysis of variance (Neter et al., 1996), classification and regression tree (Breiman et al., 1984), response surface method (Khuri Cornell, 1987), Fourier amplitude sensitivity test (FAST) (Saltelli et al., 2000), mutual information index (Jelinek, 1970) and Sobol s indices (Sobol, 1993). Examples of graphical sensitivity analysis methods include scatter plots (Kleijnen Helton, 1999) and conditional sensitivity analysis (Frey et al., 2003). Further discussion of these methods is provided in Frey Patil (2002) and Frey et al. (2003, 2004). [Pg.59]

Breiman L, Friedman JH, Stone CJ, Olshen RA (1984) Classification and regression trees. Belmont, CA, Chapman Hall/CRC Press. [Pg.85]

Once clusters were determined, the next step was to identify which measures were important in defining the clusters. A classification procedure similar to discriminant analysis was used to determine which attributes actually placed a point in a particular cluster. Because all measurements are categorical (presence or absence), a nonparametric procedure called classification and regression tree (CART) was used. [Pg.457]

Spadaccini R, Trabucco F, Saviano G, Picone D, Crescenzi O, Tancredi T, Temussi PA (2003) The mechanism of interaction of sweet proteins with the T1R2-T1R3 receptor evidence from the solution structure of G16A-MNEI. J Mol Biol 328 683-692 Spillane WJ, Kelly DP, Curran PJ, Feeney BG (2006) Structure-taste relationships for disubsti-tuted phenylsulfamate tastants using classification and regression tree (CART) analysis. J Agric Food Chem 54 5996-6004... [Pg.213]

D 3D AD ADME ADMET ANN ARD BCI BCUT BNN C4.5 CART ClogP CoMFA CV Two dimensional Three dimensional Applicability domain Absorption, distribution metabolism, and excretion Absorption, distribution metabolism, excretion, and toxicity Artificial neural network Automatic relevance determination Bernard chemical information Burden, CAS, University of Texas descriptors Bayesian neural network Decision trees using information entropy Classification and regression tree Calculated partition coefficient between octanol and water Comparative molecular field analysis Cross-validation... [Pg.375]

Abbreviations used sigmoidal regression (SR), classification and regression trees (CART), partial least square projection to latent structure... [Pg.422]


See other pages where Classification and regression tree is mentioned: [Pg.85]    [Pg.96]    [Pg.444]    [Pg.5]    [Pg.41]    [Pg.462]    [Pg.484]    [Pg.232]    [Pg.261]    [Pg.277]    [Pg.334]    [Pg.213]    [Pg.361]    [Pg.402]    [Pg.413]    [Pg.5]    [Pg.41]    [Pg.453]    [Pg.186]    [Pg.409]    [Pg.496]   
See also in sourсe #XX -- [ Pg.227 ]




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