Semi-Supervised Fuzzy-Rough Feature Selection
Metadatos
Afficher la notice complèteEditorial
Springer
Materia
Fuzzy-rough sets Feature selection Semi-supervised learning Inteligencia artificial Artificial intelligence
Date
2015-11-08Referencia bibliográfica
Jensen, R... [et al.] (2015). Semi-Supervised Fuzzy-Rough Feature Selection. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. [https://doi.org/10.1007/978-3-319-25783-9_17]
Résumé
With the continued and relentless growth in dataset sizes in
recent times, feature or attribute selection has become a necessary step
in tackling the resultant intractability. Indeed, as the number of dimensions
increases, the number of corresponding data instances required in
order to generate accurate models increases exponentially. Fuzzy-rough
set-based feature selection techniques offer great flexibility when dealing
with real-valued and noisy data; however, most of the current approaches
focus on the supervised domain where the data object labels are known.
Very little work has been carried out using fuzzy-rough sets in the areas
of unsupervised or semi-supervised learning. This paper proposes a novel
approach for semi-supervised fuzzy-rough feature selection where the
object labels in the data may only be partially present. The approach
also has the appealing property that any generated subsets are also valid
(super)reducts when the whole dataset is labelled. The experimental evaluation
demonstrates that the proposed approach can generate stable and
valid subsets even when up to 90% of the data object labels are missing.