The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the...

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of...

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an...

Reinforcement Learning (RL) is a learning framework in which an agent learns a policy from continual interaction with the environment. A policy is a mapping from states to actions. The agent receives rewards as feedback on the actions performed. The objective of RL is to design autonomous agents...

Understanding the nature and dynamics of conflicting opinions isa profound and challenging issue. In this paper we address severalaspects of the issue through a study of more than 3,000 Amazoncustomer reviews of the controversial bestseller The Da VinciCode, including 1,738 positive and 918...

Physical distribution services are becoming increasingly important as supply chains strive to become more efficient in the logistical flow of goods to industrial customers. Performance of these services, however, takes place during encounters that customers have with various interfacing...

The main goal of this contribution is to suggest to computer assembling company the optimal replacement of the most critical computer component according to criterion of the minimum guarantee repairs costs during warranty period. Guarantee repair costs were calculated using a method of decision...

This dissertation research focuses on modeling and evaluating multivariate uncertainties and the dependency between the uncertainties. Managing risk and making strategic decisions under uncertainty is critically important for both individual and corporate success. In this dissertation research,...

Over the last decade there has been growing interest in pool-based active learning techniques, where instead of receiving an i.i.d. sample from a pool of unlabeled data, a learner may take an active role in selecting examples from the pool. Queries to an oracle (a human annotator in most...

We define and investigate classes of statistical models for the analysis of associations between variables, some of which are qualitative and some quantitative. In the cases where only one kind of variables is present, the models are well-known models for either contingency tables or covariance...