We study the complexity of isomorphism testing for boolean functions that are represented by decision trees or decision lists. Our results are the following: • Isomorphism testing of rank 1 decision trees is complete for logspace. • For any constant r ≥ 2, isomorphism testing for rank r decision trees is polynomial-time equivalent to Graph Isomorphism. As a consequence of our reduction, we obta...

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowl...

We study the possibility of constructing decision trees with evolutionary algorithms in order to increase their predictive accuracy. We present a self-adapting evolutionary algorithm for the induction of decision trees and describe the principle of decision making based on multiple evolutionary induced decision trees – decision forest. The developed model is used as a fault predictive approach ...

Ensemble learning schemes have shown impressive increases in prediction accuracy over single model schemes. We introduce a new decision forest learning scheme, whose base learners are Minimum Message Length (MML) oblique decision trees. Unlike other tree inference algorithms,MMLoblique decision tree learning does not over-grow the inferred trees. The resultant trees thus tend to be shallow and ...

Because it is not known to determine a proper sample size for data mining tasks, the task of determining proper sample sizes for decision trees that are one of the best data mining algorithms is arbitrary, and as the size of samples grows, the size of generated decision trees grows with some improvement in error rates. But we cannot use larger and larger samples, because it’s not easy to unders...

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks out...

We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the decision trees evolved we can remove the unessential parts, called introns, from the discovered decision trees. Since the resulting trees contain only useful information they are smaller and easier to understand. Moreover, ...

Mainly understandable decision trees have been intended for perfect symbolic data. Conventional crisp decision trees (DT) are extensively used for classification purpose. However, there are still many issues particularly when we used the numerical (continuous valued) attributes. Structured continuouslabel classification is one type of classification in which the label is continuous in the data....

Decision trees are representations of discrete functions with widespread applications in, e.g., complexity theory and data mining and exploration. In these areas it is important to obtain decision trees of small size. The minimization problem for decision trees is known to be NP-hard. In this paper the problem is shown to be even hard to approximate up to any constant factor.

This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of WH → lνqq̄ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 90% the b-jet purity given by boosted decision trees is almost 20% higher than that given by neural networks.