Early forecasting of project dispute resolutions (PDRs) provides decision-support information for resolving potential procurement problems before a dispute occurs. This study compares the performances of classification and ensemble models for predicting dispute handling methods in public–private partnership (PPP) projects. Model analyses use machine learners (i.e., Support Vector Machines (SVMs...

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables ...

In this paper, after some introductory remarks into the classification problem as considered in various research communities, and some discussions concerning some of the reasons for ascertaining the performances of the three chosen algorithms, viz., CART (Classification and Regression Tree), C4.5 (one of the more recent versions of a popular induction tree technique known as ID3), and a multi-l...

[1] The main problem encountered when applying remote sensing and geographic information systems techniques for wildfire risk assessment is the necessity to integrate different data sources. The methods applied so far are usually based on regression techniques or on coefficients relying on experts’ knowledge. Hence fire managers are seeking an unbiased statistical model able to highlight the mu...

Description of prediction guide The women’s most recent minimum data set (MDS) (collected on all nursing home residents in the US) was the primary source of information for the algorithm (total 75 variables). The data pertained to physical functioning and activities of daily living, ambulatory status, vision status, mood and behaviour patterns, incontinence, dementia, other health conditions, a...

Numerous articles comparing performances of statistical and Neural Networks (NNs) models are available in the literature, however, very few involved Classification and Regression Tree (CART) models in their comparative studies. We perform a three-way comparison of prediction accuracy involving nonlinear regression, NNs and CART models using a continuous dependent variable and a set of dichotomo...

Statistical NLP models usually only consider coarse information and very restricted context to make the estimation of parameters feasible. To reduce the modeling error introduced by a simplified probabilistic model, the Classitication and Regression Tree (CART) method was adopted in this paper to select more discriminative features for automatic model refinement. Because the features are adopte...

Most of the land use change modelers have used to model binary land use change rather than multiple land use changes. As a first objective of this study, we compared two well-known LUC models, called classification and regression tree (CART) and artificial neural network (ANN) from two groups of data mining tools, global parametric and local non-parametric models, to model multiple LUCs. The ca...