A novel binary classification approach based on geometric semantic genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{BAKUROV:2022:SEC,
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author = "I. Bakurov and M. Castelli and F. Fontanella and
A. {Scotto di Freca} and L. Vanneschi",
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title = "A novel binary classification approach based on
geometric semantic genetic programming",
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journal = "Swarm and Evolutionary Computation",
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volume = "69",
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pages = "101028",
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year = "2022",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2021.101028",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001905",
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keywords = "genetic algorithms, genetic programming, Binary
classification, Geometric semantic genetic
programming",
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abstract = "Geometric semantic genetic programming (GSGP) is a
recent variant of genetic programming. GSGP allows the
landscape of any supervised regression problem to be
transformed into a unimodal error surface, thus it has
been applied only to this kind of problem. In a
previous paper, we presented a novel variant of GSGP
for binary classification problems that, taking
inspiration from perceptron neural networks, uses a
logistic-based activation function to constrain the
output value of a GSGP tree in the interval [0,1]. This
simple approach allowed us to use the standard RMSE
function to evaluate the train classification error on
binary classification problems and, consequently, to
preserve the intrinsic properties of the geometric
semantic operators. The results encouraged us to
investigate this approach further. To this aim, in this
paper, we present the results from 18 test problems,
which we compared with those achieved by eleven
well-known and widely classification schemes. We also
studied how the parameter settings affect the
classification performance and the use of the F-score
function to deal with imbalanced data. The results
confirmed the effectiveness of the proposed approach",
- }
Genetic Programming entries for
Illya Bakurov
Mauro Castelli
Francesco R Fontanella
Alessandra Scotto di Freca
Leonardo Vanneschi
Citations