Improving evolutionary decision tree induction with multi-interval discretization

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

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Embedded Feature Construction in Fuzzy Decision Tree Induction for High Energy Physics Classification

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9283103 ◽

2020 ◽

Author(s):

Noelie Cherrier ◽

Jean-Philippe Poli ◽

Maxime Defurne ◽

Franck Sabatie

Keyword(s):

Decision Tree ◽

High Energy Physics ◽

High Energy ◽

Feature Construction ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Decision Tree Induction ◽

Energy Physics

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A Practical Tutorial for Decision Tree Induction

ACM Computing Surveys ◽

10.1145/3429739 ◽

2021 ◽

Vol 54 (1) ◽

pp. 1-38

Author(s):

Víctor Adrián Sosa Hernández ◽

Raúl Monroy ◽

Miguel Angel Medina-Pérez ◽

Octavio Loyola-González ◽

Francisco Herrera

Keyword(s):

Decision Tree ◽

Decision Trees ◽

Machine Learning Techniques ◽

Evaluation Measures ◽

Decision Tree Induction ◽

Learning Techniques ◽

Tree Models ◽

Evaluation Measure ◽

Main Components ◽

Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

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