A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the MetropolisHastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance pro...

RATIONALE AND OBJECTIVES Decision support systems have the capacity to improve diagnostic performance and reduce physician errors. The purpose of this study was to evaluate the use of classification and regression trees (CART) with bootstrap aggregation as a decision support system in the baseline plus furosemide (F + 20) diuresis renography protocol to determine when obstruction can be exclude...

ÐA fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fu...

Learning a function fX→Y that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (X,Y) have to be known before a model can be trained. When this is not the case, or when functions fX→Y are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of ...

Hyper-spectral remote sensing increases the volume of information available for research and practice, but brings with it the need for efficient statistical methods in sample spaces of many dimensions. Due to the complexity of problems in high dimensionality, several methods for dimension reduction are suggested in the literature, such as Principal Components Analysis (PCA). Although PCA can be...

Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision trees specific for imbalanced data (CCPDT: Clas...

This paper evaluates and predicts a certain epidemiological (cancer survival) condition using data-mining techniques in SAS®. A data set that contains information about the survival of lung-cancer patients from a study at the Mayo Clinic was extracted from the R survival package. Data-mining techniques—namely linear and logistic regression models, regression and classification trees, and neares...

BACKGROUND Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia....