Classification And Regression Trees CART

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. 

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... 

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

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. 

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... 

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

Besides the classical Discriminant Analysis (DA) and the k-Nearest Neighbor (k-NN), other classification methods widely used in QSAR/QSPR studies are SIMCA, Linear Vector Quantization (LVQ), Partial Least Squares-Discriminant Analysis (PLS-DA), Classification and Regression Trees (CART), and Cluster Significance Analysis (CSA), specifically proposed for asymmetric classification in QSAR. 

The scale-error-complexity (SEC) surfaces. Instead of observing the prediction error with respect to resolution, it is also possible to monitor the complexity of the calibration/classification model. In PLS this can be measured by the number of PLS factors needed. How the error (e.g. RMSECV, RMSEP, PRESS) changes with varying the added scale and model complexity can be observed in scale-error-complexity (SEC) surfaces. In this case the first axis is the scales, the second axis is the model complexity (for PLS this is the number factors) and the third axis is the error. The complexity dimension is not limited to the number of PLS factors. For example classification and regression trees (CART) a measure based on tree depth and branching could be used [45],... 

Romisch et al. in 2009 presented a study on the characterization and determination of the geographical origin of wines. In this paper, three methods of discrimination and classification of multivariate data were considered and tested the classification and regression trees (CART), the regularized discriminant analysis (RDA) and the partial least squares discriminant analysis (PLS-DA). PLS-DA analysis showed better classification results with percentage of correct classified samples from 88 to 100%. 

The most frequently used supervised pattern recognition method is the linear discriminant analysis (LDA), not to be confused with its twin brother canonical correlation analysis (CCA) or canonical variate analysis (CVA). Recently, classification and regression trees (CART) produced surprisingly good results. Artificial neural networks (ANNs) can be applied for both prediction and pattern recognition (supervised and unsupervised). 

Berk, R. A. (2008). Classification and Regression Trees (CART). Statisticallearningfrom a regression perspective. New York Springer. 

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