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2008
ACM

Feature selection via sensitivity analysis of SVM probabilistic outputs

8 years 2 months ago
Feature selection via sensitivity analysis of SVM probabilistic outputs
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ ...
Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where ML
Authors Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar P. V. Wilder-Smith
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