Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

Decision tree techniques have been widely used to build classification models as such
models closely resemble human reasoning and are easy to understand. This paper …

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy
classification. The approaches proposed so far to FDT learning, however, have generally …

Besides serving as prediction models, classification trees are useful for finding important
predictor variables and identifying interesting subgroups in the data. These functions can be …

We discuss a novel decision tree architecture with soft decisions at the internal nodes where
we choose both children with probabilities given by a sigmoid gating function. Our algorithm …

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means
Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is …

Decision trees are popular as stand-alone classifiers or as base learners in ensemble
classifiers. Mostly, this is due to decision trees having the advantage of being easy to …

The random forests method is one of the most successful ensemble methods. However,
random forests do not have high performance when dealing with very-high-dimensional …

This paper presents a new approach that avoids the over-fitting and complexity problems
suffered in the construction of decision trees. Decision trees are an efficient means of …

We discuss approaches to incrementally construct an ensemble. The first constructs an
ensemble of classifiers choosing a subset from a larger set, and the second constructs an …