A grammatical evolution based hyper-heuristic for the automatic design of split criteria

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Basgalupp:2014:GECCO,

• author = "Marcio Porto Basgalupp and Rodrigo Coelho Barros and Tiago Barabasz",
• title = "A grammatical evolution based hyper-heuristic for the automatic design of split criteria",
• booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2662-9",
• pages = "1311--1318",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2576768.2598327",
• DOI = "doi:10.1145/2576768.2598327",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Top-down induction of decision trees (TDIDT) is a powerful method for data classification. A major issue in TDIDT is the decision on which attribute should be selected for dividing the nodes in subsets, creating the tree. For performing such a task, decision trees make use of a split criterion, which is usually an information-theory based measure. Apparently, there is no free-lunch regarding decision-tree split criteria, as is the case of most things in machine learning. Each application may benefit from a distinct split criterion, and the problem we pose here is how to identify the suitable split criterion for each possible application that may emerge. We propose in this paper a grammatical evolution algorithm for automatically generating split criteria through a context-free grammar. We name our new approach ESC-GE (Evolutionary Split Criteria with Grammatical Evolution). It is empirically evaluated on public gene expression datasets, and we compare its performance with state-of-the-art split criteria, namely the information gain and gain ratio. Results show that ESC-GE outperforms the baseline criteria in the domain of gene expression data, indicating its effectiveness for automatically designing tailor-made split criteria.",
• notes = "Also known as \cite{2598327} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",

}

Genetic Programming entries for Marcio Porto Basgalupp Rodrigo C Barros Tiago Barabasz

Citations