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Pattern Analysis and Applications

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14.08.2023 | Theoretical Advances

CInf-FS\(_S\): an efficient infinite feature selection method using K-means clustering to partition large feature spaces

verfasst von: Seyyedeh Faezeh Hassani Ziabari, Sadegh Eskandari, Maziar Salahi

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2023

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Abstract

In this paper, we present a new feature selection algorithm for supervised problems. We build our algorithm upon recently proposed infinite feature selection (Inf-FS) method where features are ranked based on path integrals and the centrality concept on a feature adjacency graph. The proposed algorithm firstly clusters the feature space into a predefined number of subspaces. Then ranks the features in each subspace using Inf-FS method. Finally merges the resultant subranks using a combined measure of information theory and clusters size. We extensively evaluate our algorithm on six benchmark datasets, and show that our method outperforms Inf-FS in terms of the accuracy, running time, and memory consumption. The codes are available at https://github.com/Sadegh28/CInf-FS.

Vorheriger Artikel A multi-metric small sphere large margin method for classification

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Titel: CInf-FS: an efficient infinite feature selection method using K-means clustering to partition large feature spaces
verfasst von: Seyyedeh Faezeh Hassani Ziabari
Sadegh Eskandari
Maziar Salahi
Publikationsdatum: 14.08.2023
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 4/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-023-01189-1

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