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Decision Trees
Decision trees are nonlinear graphical models that have found important applications in machine learning mainly due to their interpretability as well...
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Bounds on depth of decision trees derived from decision rule systems with discrete attributes
Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are...
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Efficient Lookahead Decision Trees
Conventionally, decision trees are learned using a greedy approach, beginning at the root and moving toward the leaves. At each internal node, the...
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Decision Trees
This video brings out the importance of decision tree algorithm in classical machine learning teaching you how to construct and traverse the decision...
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Decision Trees
Decision tree is probably the most intuitive data classification and prediction method. It is also used frequently. While most of the data mining...
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Optimal multivariate decision trees
Recently, mixed-integer programming (MIP) techniques have been applied to learn optimal decision trees. Empirical research has shown that optimal...
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Data Classification with Decision Trees
In this chapter, we will develop the theory for binary decision trees. Decision trees can be used to classify data and fall into the Learning...
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Obfuscating Evasive Decision Trees
We present a new encoder for hiding parameters in an interval membership function. As an application, we design a simple and efficient virtual...
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Efficient Modal Decision Trees
Modal symbolic learning is an emerging machine learning paradigm for (non)-tabular data, and modal decision trees are its most representative schema....
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Single MCMC chain parallelisation on decision trees
Decision trees (DT) are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like...
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Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data...
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Interpretable decision trees through MaxSAT
We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply...
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Soft Decision Trees
In this chapter, we develop the foundation of a new theory for decision trees based on new modeling of phenomena with soft numbers. Soft numbers...
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Optimizing Decision Trees for Enhanced Human Comprehension
This paper studies a novel approach for training people to perform complex classification tasks using decision trees. The main objective of this...
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Time and space complexity of deterministic and nondeterministic decision trees
In this paper, we study arbitrary infinite binary information systems each of which consists of an infinite set called universe and an infinite set...
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Algebraically explainable controllers: decision trees and support vector machines join forces
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they...
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Using Decision Trees for Interpretable Supervised Clustering
In this paper, we address an issue of finding explainable clusters of class-uniform data in labeled datasets. The issue falls into the domain of...
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Learning from crowds with decision trees
Crowdsourcing systems provide an efficient way to collect labeled data by employing non-expert crowd workers. In practice, each instance obtains a...
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Decision Trees with a Modal Flavor
Symbolic learning is the sub-field of machine learning that deals with symbolic algorithms and models, which have been known for decades and...
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Regularized impurity reduction: accurate decision trees with complexity guarantees
Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model...
