In this paper, we study decision trees, which solve problems defined over a specific subclass of infinite information systems, namely: 1-homogeneous binary systems. It is proved that the minimum depth tree (defined as function on number attributes in problem’s description) grows – worst case logarithmically or linearly for each system class. We consider examples including one closely related to...

This research was sponsored in part by U S WEST and in part by the Defense Advanced Research Projects Agency (DOD), and monitored by the Space and Naval Warfare Systems Command under Contract N0003985-C-0163, ARPA Order No. 5167. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or ...

The main goal of this paper is to present and evaluate the possibility of using the methods and tools of Artificial Intelligence and Data Mining to analyze marketing data needed to support decision-making in the process of market segmentation. This paper describes the application of Kohonen’s Neural Networks and Classification Trees (including tools such as CART-Classification and Regression Tr...

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences landslide occurrence. Thus, mitigation and prevention been topical issues. Thereinto, numerous research achievements on susceptibility assessment springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional ...

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and ...

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...

To limit disclosure risks, one approach is to release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple imputations. This article presents and evaluates the use of classification and regression trees to generate p...

Decision tree methods generally suppose that the number of categories of the attribute to be predicted is fixed. Breiman et al., with their Twoing criterion in CART, considered gathering the categories of the predicted attribute into two superclasses. In this paper, we propose an extension of this method. We try to merge the categories in an optimal unspecified number of superclasses. Our metho...

In the era of information technology, work relies on technology to generate a huge amount data and information. Among them, financial universities is growing exponentially, manual method organizing extracting key can no longer meet requirements management universities. Taking higher education institutions as an example, it difficult grasp progress budget execution with frequent complicated dail...

background & aim: in many medical studies, one data set is used to construct the model, and to test its performance. this approach is prone to over optimization, and leads to statistics with low chance of external validity. data splitting can be used to create training and test sets but the cost is reduction in power. the aim of this study was to demonstrate the ability of bootstrap aggregating...