Abstract Classification and regression trees, as well their variants, are off-the-shelf methods in Machine Learning. In this paper, we review recent contributions within the Continuous Optimization Mixed-Integer Linear paradigms to develop novel formulations research area. We compare those terms of nature decision variables constraints required, optimization algorithms proposed. illustrate how ...

Abstract In this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account noisy labels may occur in the training sample. The motivation of new is based on superaditive effect combining together margin classifiers and outlier detection techniques. Our approach rests two main elements: (1) splitting rules for classification trees are designed maximize...

Classification trees are a popular statistical tool with multiple applications. Recent advancements of traditional classification trees, such as the approach of classification trees based on imprecise probabilities by Abellán and Moral (2004), effectively address their tendency to overfitting. However, another flaw inherent in traditional classification trees is not eliminated by the imprecise ...

Classification trees are a popular statistical tool with multiple applications. Recent advancements of traditional classification trees, such as the approach of classification trees based on imprecise probabilities by Abellán and Moral (2005), effectively address their tendency to overfitting. However, another flaw inherent in traditional classification trees is not eliminated by the imprecise ...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...