|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 8 van 12 gevonden artikelen
| Titel: |
Local feature selection using Gaussian process regression |
| Auteur: |
Pichara, Karim Soto, Alvaro |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 18 (2014) nr. 3 pagina's 319-336 |
| Jaar: |
2014-05-14 |
| Inhoud: |
Most feature selection methods determine a global subset of features, where all data instances are projected in order to improve classification accuracy. An attractive alternative solution is to adaptively find a local subset of features for each data instance, such that, the classification of each instance is performed according to its own selective subspace. This paper presents a novel application of Gaussian Processes (GPs) that improves classification performance by learning a set of functions that quantify the discriminative power of each feature. Specifically, GP regressions are used to build for each available feature a function that estimates its discriminative properties over all its input space. Afterwards, by locally joining these regressions it is possible to obtain a discriminative subspace for any position of the input space. New instances are then classified by using a K-NN classifier that operates in the local subspaces. Experimental results show that by using local discriminative subspaces, we are able to reach higher levels of classification accuracy than alternative state-of-the-art feature selection approaches. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 8 van 12 gevonden artikelen
| << vorige | volgende >> |