Pruning Decision Trees using Rules3 Inductive Learning Algorithm

Pruning Decision Trees and Lists

Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. Decision trees and lists are potentially powerful predictors and embody an explicit representation of the struc...

Pruning Decision Trees and Lists

New algorithms for learning and pruning oblique decision trees

In this paper, we present methods for learning and pruning oblique decision trees. We propose a new function for evaluating different split rules at each node while growing the decision tree. Unlike the other evaluation functions currently used in literature (which are all based on some notion of purity of a node), this new evaluation function is based on the concept of degree of linear separab...

Using Impurity and Depth for Decision Trees Pruning

Most pruning methods for decision trees minimize a classification error rate. In uncertain domains, some subtrees which do not lessen the error rate can be relevant to point out some populations of specific interest or to give a representation of a large data file. We propose here a new pruning method (called pruning) which takes into account the complexity of sub-trees and which is able to kee...

Learning Invariants using Decision Trees

The problem of inferring an inductive invariant for verifying program safety can be formulated in terms of binary classification. This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets. Here, the good points are the reachable states of the program, and the bad points are...