Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification

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@Article{Barros:2015:GPEM,

• author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C. P. L. F. {de Carvalho}",
• title = "Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2015",
• volume = "16",
• number = "3",
• pages = "241--281",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Hyper-heuristics, Decision trees, Fitness function, Imbalanced data",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-014-9235-z",
• size = "41 pages",
• abstract = "In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.",
• notes = "Author Affiliations

  1. Faculdade de Informatica (FACIN), Pontificia Universidade Catolica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil 2. Instituto de Ciencia e Tecnologia (ICT), Universidade Federal de Sao Paulo (UNIFESP), Sao Josedos Campos, Brazil 3. Instituto de Ciencias Matematicas e de Computacao (ICMC), Universidade de Sao Paulo (USP), Sao Carlos, Brazil

  ",

}

Genetic Programming entries for Rodrigo C Barros Marcio Porto Basgalupp Andre Ponce de Leon F de Carvalho

Citations