This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Decision trees have proved to be valuable tools for the description, classification and
generalization of data. Work on constructing decision trees from data exists in multiple …

State-of-the-art decision tree methods apply heuristics recursively to create each split in
isolation, which may not capture well the underlying characteristics of the dataset. The …

Recent results have used game theory to explore the nature of optimal investments in the
security of simple series and parallel systems. However, it is clearly important in practice to …

We apply game theory, optimization, and reliability analysis to identify optimal defenses
against intentional threats to system reliability. The goals are to identify optimal strategies for …

On growing better decision trees from data On growing better decision trees from data Abstract
This thesis investigates the problem of growing decision trees from data, for the purposes of …

For the past 30 years, decision tree methods have been one of the most widely-used
approaches in machine learning across industry and academia, due in large part to their …

We consider the problem of testing sequentially the components of a multi-component
system in order to learn the state of the system, when the tests are costly. In this review …

5 Conclusions As noted above, protecting engineered systems against intentional attacks is
likely to require a combination of game theory and reliability analysis. Risk and reliability …

Most existing decision tree systems use a greedy approach to induce trees--locally optimal
splits are induced at every node of the tree. Although the greedy approach is suboptimal, it is …