RT Journal Article
T1 Evolutionary feature selection on high dimensional data using a search space reduction approach
A1 García Torres, Miguel
A1 Ruiz, Roberto
A1 Divina, Federico
K1 Feature selection
K1 Scatter Search
K1 Feature grouping
AB Feature selection is becoming more and more a challenging task due to the increase of the dimensionality of the data. The complexity of the interactions among features and the size of the search space make it unfeasible to find the optimal subset of features. In order to reduce the search space, feature grouping has arisen as an approach that allows to cluster feature according to the shared information about the class. On the other hand, metaheuristic algorithms have proven to achieve sub-optimal solutions within a reasonable time. In this work we propose a Scatter Search (SS) strategy that uses feature grouping to generate an initial population comprised of diverse and high quality solutions. Solutions are then evolved by applying random mechanisms in combination with the feature group structure, with the objective of maintaining during the search a population of good and, at the same time, as diverse as possible solutions. Not only does the proposed strategy provide the best subset of features found but it also reduces the redundancy structure of the data. We test the strategy on high dimensional data from biomedical and text-mining domains. The results are compared with those obtained by other adaptations of SS and other popular strategies. Results show that the proposed strategy can find, on average, the smallest subsets of features without degrading the performance of the classifier
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10433/19660
UL https://hdl.handle.net/10433/19660
LA en
NO Engineering Applications of Artificial Intelligence, vol. 117, p. 105556
NO Proyectos de investigaciónFECYT -- APRENDIZAJE PROFUNDO Y APRENDIZAJE ONLINE EXPLICABLES PARA SOST...PY20-00870UPO-138516
NO Deporte e Informática
DS RIO
RD Apr 24, 2026