Multi-Objective Genetic Programming for Dataset Similarity Induction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Smid:2015:ieeeSSCI,
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author = "Jakub Smid and Martin Pilat and Klara Peskova and
Roman Neruda",
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booktitle = "2015 IEEE Symposium Series on Computational
Intelligence",
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title = "Multi-Objective Genetic Programming for Dataset
Similarity Induction",
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year = "2015",
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pages = "1576--1582",
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month = "7-10 " # dec,
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address = "Cape Town, South Africa",
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keywords = "genetic algorithms, genetic programming, Metadata,
Measurement, Optimization, Prediction algorithms,
Correlation, Electronic mail",
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isbn13 = "978-1-4799-7560-0",
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DOI = "doi:10.1109/SSCI.2015.222",
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size = "7 pages",
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abstract = "Metal earning - the recommendation of a suitable
machine learning technique for a given dataset - relies
on the concept of similarity between datasets.
Traditionally, similarity measures have been
constructed manually, and thus could not precisely
grasp the complex relationship among the different
features of the datasets. Recently, we have used an
attribute alignment technique combined with genetic
programming to obtain more fine-grained and trainable
dataset similarity measure. In this paper, we propose
an approach based on multi-objective genetic
programming for evolving an attribute similarity
function. Multi-objective optimisation is used to
encourage some of the metric properties, thus
contributing to the generalisation abilities of the
similarity function being evolved. Experiments are
performed on the data extracted from the OpenML
repository and their results are compared to the
baseline algorithm.",
-
notes = "Also known as \cite{7376798}",
- }
Genetic Programming entries for
Jakub Smid
Martin Pilat
Klara Peskova
Roman Neruda
Citations