Objective This study aimed to identify the major variables in the estimation of health expenditure in OECD member countries with the decision tree method and to categorize the member countries by health expenditure. Methods The study population comprised the 2014 data of the 35 OECD countries. In the study, health expenditure as a share of gross domestic product was the dependent variable whi...

Present speech recognition systems are becoming more complex due to technology advances, optimizations and special requirements such as small computation and memory footprints. Proper handling of system failures can be seen as a kind of fault diagnosis. Motivated by the success of decision tree diagnosis in other scientific fields and by their successful application in speech recognition in the...

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...

Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classi cation accuracy, training time, and (in the case of trees) number of leaves. Classi cation accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, spline-based, algorithm called Polyclass at the top, although it is n...

This paper provides a survey of numerous data mining classification techniques for innovative database applications. Classification is a model finding process that is used for assigning the data into different classes according to specific constrains. There are several major kinds of classification algorithms including Genetic algorithm C4.5, Naive Bayes, SVM, KNN, decision tree and CART. We de...

Because it is not known to determine a proper sample size for data mining tasks, the task of determining proper sample sizes for decision trees that are one of the best data mining algorithms is arbitrary, and as the size of samples grows, the size of generated decision trees grows with some improvement in error rates. But we cannot use larger and larger samples, because it’s not easy to unders...

In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. In this paper, we conducted experiment in the WEKA environment by using four algorithms namely I...

We present advances in Bayesian modeling and computation for CART (classification and regression tree) models. The modeling innovations include a formal prior distributional structure for tree generation – the pinball prior – that allows for the combination of an explicit specification of a distribution for both the tree size and the tree shape. The core computational innovations involve a nove...

For classification problems in psychology (e.g., clinical diagnosis), batteries of tests are often administered. However, not every test or item may be necessary for accurate classification. In the current article, a combination of classification and regression trees (CART) and stochastic curtailment (SC) is introduced to reduce assessment length of questionnaire batteries. First, the CARTalgor...