A lexicographic cooperative co-evolutionary approach for feature selection
Metadatos
Mostrar el registro completo del ítemAutor
González Peñalver, Jesús; Ortega Lopera, Julio; Escobar Pérez, Juan José; Damas Hermoso, MiguelEditorial
Elsevier
Materia
Cooperative co-evolution Multi-objective optimization Lexicographic optimization Feature selection Classification
Fecha
2021-08-11Referencia bibliográfica
Jesús González... [et al.]. A lexicographic cooperative co-evolutionary approach for feature selection, Neurocomputing, Volume 463, 2021, Pages 59-76, ISSN 0925-2312, [https://doi.org/10.1016/j.neucom.2021.08.003]
Patrocinador
Spanish "Ministerio de Ciencia, Innovacion y Universidades" PGC2018-098813-B-C31; European Commission; Universidad de Granada/CBUAResumen
This paper starts with two hypotheses. The first one is that the simultaneous optimization of the
hyperparameters regulating the classifier within a wrapper method, while the best subset of features
is being determined, should improve the results with respect to those obtained with a preparameterized
classifier. The second one is that solving these two problems can be formulated as a lexicographic
optimization problem, allowing the use of a simple single-objective evolutionary algorithm to
solve this multi-objective problem.
The fitness function is of key importance for such wrapper methods. It is responsible for guiding the
search towards potentially good solutions and it also consumes most of the runtime. Having these issues
in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime
of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the
results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity
thresholds used when comparing two different solutions lexicographically and the percentage of
data samples used for validation during the training process. Thus, an experimental analysis has been
carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative
co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested
with several datasets belonging to the University of California, Irvine (UCI) repository and also with some
real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper
methods. The comparison has also been made lexicographically, with a new methodology proposed in
this paper.