Learning decision trees from random examples

Learning Structural Decision Trees from Examples

STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorithm to construct new features in a first-order representation. Experiments compare the effects of different hypothesis evaluation strategies, domain representation, and feature construction. STRUCT is also compared with Quinlan's ...

Random Decision Trees

Learning from Examples: Generation and Evaluation of Decision Trees for Software Resource Analysis

Solutions to the problem of learning from examples will have far-reaching bene ts, and therefore, the problem is one of the most widely studied in the eld of machine learning. The purpose of this study is to investigate a general solution method for the problem, the automatic generation of decision (or classi cation) trees. The approach is to provide insights through in-depth empirical characte...

Learning Random Log-Depth Decision Trees under the Uniform Distribution

We consider three natural models of random log-depth decision trees. We give an efficient algorithm that for each of these models learns—as a decision tree—all but an inverse polynomial fraction of such trees using only uniformly distributed random examples.

Learning Decision Trees From Histogram Data

When applying learning algorithms to histogram data, bins of such variables are normally treated as separate independent variables. However, this may lead to a loss of information as the underlying dependencies may not be fully exploited. In this paper, we adapt the standard decision tree learning algorithm to handle histogram data by proposing a novel method for partitioning examples using bin...