Automatic Design of Decision-Tree Induction Algorithms

  title={Automatic Design of Decision-Tree Induction Algorithms},
  author={Rodrigo C. Barros and Andr{\'e} Carlos Ponce de Leon Ferreira de Carvalho and Alex Alves Freitas},
  booktitle={SpringerBriefs in Computer Science},
DEcision-tree induction is one of the most employed methods to extract knowledge from data. There are several distinct strategies for inducing decision trees from data, each one presenting advantages and disadvantages according to its corresponding inductive bias. These strategies have been continuously improved by researchers over the last 40 years. This thesis, following recent breakthroughs in the automatic design of machine learning algorithms, proposes to automatically generate decision… 

A Practical Tutorial for Decision Tree Induction

This article introduces a tutorial that explains decision tree induction, and presents 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.

Estimation of distribution algorithms for decision-tree induction

A novel Estimation of Distribution Algorithm (EDA) for decision-tree induction, namely Ardennes is presented, which shows the feasibility of using EDAs as a means to avoid the typical greedy strategy and to prevent convergence to local optima.

An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)

The EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA.

Computational Complexity Analysis of Decision Tree Algorithms

This work theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART) to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets.

Decision Tree and Decision Forest Algorithms: On Improving Accuracy, Efficiency and Knowledge Discovery

Several novel algorithms for improving accuracy of decision trees and decision forests are proposed, a technique to reduce the size of decision forests while retaining or increasing the ensemble accuracy is proposed, and a framework for effective knowledge discovery from decision forests is proposed.

Automated design of genetic programming of classification algorithms.

The hypothesis that automating the design of GP classification algorithms for data classification can still lead to the induction of effective classifiers is investigated, and the automated designed classifiers were found to outperform the manually designed GP classifiers on all the problems considered in this study.

A multi-objective evolutionary approach to Pareto-optimal model trees

The goal of this paper is to demonstrate how a set of non-dominated model trees can be obtained using the Global Model Tree (GMT) system and proposed Pareto approach for GMT allows the decision maker to select desired output model according to his preferences on the conflicting objectives.

he role of decision tree representation in regression problems – An volutionary perspective

The objective of this paper is to demonstrate the impact of particular representations on the induced decision trees with a new evolutionary volutionary algorithms ata mining egression trees elf-adaptable representation algorithm for the decision tree induction with a structure that can self-adapt to the currently analyzed data.



Towards the automatic design of decision tree induction algorithms

This work proposes two different approaches for automatically generating generic decision tree induction algorithms based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes.

A beam search based decision tree induction algorithm

The authors of this chapter present a new algorithm that seeks to avoid being trapped in local-optima by doing a beam search during the decision tree growth by keeping the comprehensibility of the traditional methods and is much less time-consuming than evolutionary algorithms.

Lexicographic multi-objective evolutionary induction of decision trees

A new GA-based algorithm based on a lexicographic multi-objective approach for decision tree induction that is able to avoid the previously described problems, reporting accuracy gains and induced models with a significantly reduction in the complexity considering tree sizes.

On the induction of decision trees for multiple concept learning

This dissertation makes four contributions to the theory and practice of the top-down non-backtracking induction of decision trees for multiple concept learning, and analyzes the merits and limitations of using the entropy measure (and others from the family of impurity measures) for attribute selection.

A Survey of Evolutionary Algorithms for Decision-Tree Induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction, which provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach.

Evolution of Decision Trees

This work proposes to induce a decision trees (without regarding the type) with an unified algorithm based on artificial evolution, suggesting that Evolutionary Algorithms are competitive and robust for inducing all kinds of decision trees, achieving sometimes better performance than traditional algorithms.

A bottom-up oblique decision tree induction algorithm

A novel bottom-up algorithm for inducing oblique trees named BUTIA, which does not require an impurity-measure for dividing nodes, since it knows a priori the data resulting from each split.

Genetic algorithm based multiple decision tree induction

  • Z. BandarH. Al-AttarD. Mclean
  • Computer Science
    ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378)
  • 1999
The authors investigate the use of multiple DT models as a method of overcoming the limitations of the DT modeling language and describe a new and novel algorithm to automatically generate multipleDT models from the same training data.