The bio-molecular diagnosis of malignancies represents a difficult learning task, because of the high dimensionality and low cardinality of the data. Many supervised learning techniques, among them support vector machines, have been experimented, using also feature selection methods to reduce the dimensionality of the data. In alternative to feature selection methods, we proposed to apply random subspace ensembles, reducing the dimensionality of the data by randomly sampling subsets of features and improving accuracy by aggregating the resulting base classifiers. In this paper we experiment the combination of random subspace with feature selection methods, showing preliminary experimental results that seem to confirm the effectiveness of the proposed approach.
Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies / A. Bertoni, R. Folgieri, G. Valentini - In: Biological and artificial intelligence environments : 15th Italian workshop on neural nets, WIRN VIETRI 2004 / / [a cura di] Bruno Apolloni, Maria Marinaro and Roberto Tagliaferri. - Dordrecht : Springer, 2005. - ISBN 9781402034312. - pp. 29-35 [10.1007/1-4020-3432-6_4]
Feature selection combined with random subspace ensemble for gene expression based diagnosis of malignancies.
A. BertoniPrimo
;R. FolgieriSecondo
;G. ValentiniUltimo
2005
Abstract
The bio-molecular diagnosis of malignancies represents a difficult learning task, because of the high dimensionality and low cardinality of the data. Many supervised learning techniques, among them support vector machines, have been experimented, using also feature selection methods to reduce the dimensionality of the data. In alternative to feature selection methods, we proposed to apply random subspace ensembles, reducing the dimensionality of the data by randomly sampling subsets of features and improving accuracy by aggregating the resulting base classifiers. In this paper we experiment the combination of random subspace with feature selection methods, showing preliminary experimental results that seem to confirm the effectiveness of the proposed approach.Pubblicazioni consigliate
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