We introduce a probabilistic graphical model for supervised learning on databases with categorical attributes. The proposed graph contains hidden variables that play a role similar to nodes in decision trees and each of their states either corresponds to a class label or to a single attribute test. As a major diierence with respect to decision trees, the selection of the attribute to be tested ...

This paper presents Newton trees, a redefinition of probability estimation trees (PET) based on a stochastic understanding of decision trees that follows the principle of attraction (relating mass and distance through the Inverse Square Law). The structure, application and the graphical representation of Newton trees provide a way to make their stochastically driven predictions compatible with ...

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...

This paper presents the problem of finding parameter settings of algorithms for building decision trees that yield optimal trees—accurate and small. The problem is tackled using DEMO algorithm, an evolutionary algorithm for multiobjective optimization that uses differential evolution to explore the decision space. The results of the experiments on six datasets show that DEMO is capable of effic...

We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that a ect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some train...

In many cases it is better to extract a set of decision trees and a set of possible logical data descriptions instead of a single model. The trees that include premises with constraints on the distances from some reference points are more flexible because they provide nonlinear decision borders. Methods for creating heterogeneous forests of decision trees based on Separability of Split Value (S...

Decision Trees are powerful and popular tools for prediction. These trees are especially useful where in addition to the accuracy of the prediction, the reason for the prediction is also important. This paper presents an application where these decision trees would be used to predict the gross income of a bollywood movie. The paper identifies several factors influencing a movie's income an...

This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over features in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available information; constructed ensembles of decision trees based on different feature subspaces show signi...

The explanation of heterogeneous multivariate time series data is a central problem in many applications. The problem requires two major data mining challenges to be addressed simultaneously: Learning models that are humaninterpretable and mining of heterogeneous multivariate time series data. The intersection of these two areas is not adequately explored in the existing literature. To address ...

Decision trees classifiers are popular classification methods. In this paper, we extend to multi-class problems a decision tree method based on belief functions previously described for 2-class problems only. We propose two ways to achieve this extension: combining multiple 2-class trees together and directly extending the estimation of belief functions within the tree to the multi-class settin...