Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment and configuration. Based on bagging ensemble, we proposed an algorithm named selective bagging classification and regression tree (SBCART) to model storage device performance. In addition, we consider the caching effect as a feature in workload character...

I Abstract — Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports (e.g., Kattan et al., 1998) show that ANN and CART models can perform better than classic ...

This paper reports preliminary results of data-driven modeling of segmental (phoneme) duration for Hindi. Classification and Regression Tree (CART) based datadriven duration modeling for segmental duration prediction is presented. A number of features are considered and their usefulness and relative contribution for segmental duration prediction is assessed. Objective evaluation of the duration...

Vehicle occupants comprise a considerable proportion of traffic crash victims in Iran. This paper has focused on vehicleoccupants’ injury severity and employed the Classification and Regression Tree (CART) technique in order toidentify the most important variables affecting the injury severity of these road users in crashes occurred on rural freewaysand multilane highways in I...

OBJECTIVES Demonstrate the application of decision trees--classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)--to understand structure in missing data. SETTING Data taken from employees at 3 different industrial sites in Australia. PARTICIPANTS 7915 observations were included. MATERIALS AND METHODS The approach was evaluated using an occupationa...

In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information, and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources, parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric cla...

There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher’s as well as policy maker’s point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding...

In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), a...

BACKGROUND Inherited variability in genes that influence androgen metabolism has been associated with risk of prostate cancer. The objective of this analysis was to evaluate interactions for prostate cancer risk by using classification and regression tree (CART) models (i.e., decision trees), and to evaluate whether these interactive effects add information about prostate cancer risk prediction...

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports (e.g., Kattan et al., 1998) show that ANN and CART models can perform better than classic regression mo...