We have build several classification tree models with different misclassification costs in the algorithm.
The algorithms compared included several variants of linear discriminant analysis, nearest neighbour classification and several variants of classification trees.
Bumping was successfully applied in combination with several learning algorithms including Classification Trees, Linear Regression, Splines and parametric density estimation [ 6], Linear Discriminant Analyis (LDA) [ 29], Neural Networks [ 30] and Self Organizing Maps (SOM) [ 31].
Several simple recursive partition (RP) classification trees were developed to differentiate between CNS drugs and non-CNS oral drugs.
Note.-The accuracies are reported for several classification models; including, best hit BLAST, the decision tree analysis, SWDAS, and CVAS.
Classification trees allow the visualization of models predicting response based on several variables.
Several classification methods, including logistic regression model, random forest, support vector machine (SVM), and Bayes tree, were used to construct effective diagnosis models for cancer prediction based on MSD-SNuPET results.
Classification trees are another, less pervasive, method that can be used to discriminate for a categorical response based on several, possibly interacting covariates [ 14, 15].
However, classification trees can suffer from limited accuracy.
For classification trees, we used Weka3 software.5.5
