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Automatic design of decision-tree algorithms with evolutionary algorithms.

Rodrigo C Barros1, Márcio P Basgalupp, André C P L F de Carvalho

  • 1Universidade de São Paulo, São Carlos, Brazil rcbarros@icmc.usp.br.

Evolutionary Computation
|January 24, 2013
PubMed
Summary
This summary is machine-generated.

A new evolutionary algorithm, HEAD-DT, automatically designs decision-tree induction algorithms. These AI-generated algorithms outperform traditional methods like C4.5 and CART in classification tasks.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Mining

Background:

  • Top-down decision-tree algorithms are crucial for intuitive and accurate classification.
  • Extensive research exists in manually designing these algorithms over 40 years.
  • Automating decision-tree algorithm design is a timely advancement.

Purpose of the Study:

  • To empirically analyze a hyper-heuristic evolutionary algorithm for automatic decision-tree induction algorithm design.
  • To evaluate the performance of HEAD-DT against established decision-tree algorithms.

Main Methods:

  • Development and testing of the HEAD-DT (Hyper-heuristic Evolutionary Algorithm for Decision Trees) algorithm.
  • Extensive empirical evaluation on 20 UCI datasets and 10 microarray gene expression datasets.
  • Comparison of HEAD-DT generated algorithms with C4.5 and CART algorithms.

Main Results:

  • HEAD-DT successfully designed novel top-down decision-tree induction algorithms.
  • Algorithms generated by HEAD-DT demonstrated significantly higher accuracy compared to C4.5 and CART.
  • The study validates the effectiveness of hyper-heuristic approaches in automating algorithm design.

Conclusions:

  • Automated design of decision-tree induction algorithms using HEAD-DT is feasible and effective.
  • HEAD-DT offers a promising approach to advancing classification algorithm development.
  • The generated algorithms provide a competitive alternative to traditional, manually designed methods.