Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation. (14th February 2011)

• Record Type: Journal Article
• Authors: Polajnar, Tamara; Rogers, Simon; Girolami, Mark
• Is Part Of: International journal of data mining and bioinformatics. Volume 5:Number 1(2011)
• Page Start: 52
• Page End: 72
• Publication Date: 2011-02-14
• Subjects: text mining -- Gaussian process classifiers -- SVM -- support vector machines -- protein interaction detection -- biomedical publications -- sentence classification -- text classification; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

The non-parametric deterministic Support Vector Machines (SVMs) produce high levels of performances in text classification. This article offers a much needed evaluation of the Gaussian Process (GP) classifier, as a non-parametric probabilistic analogue to SVMs, which has been rarely applied to text classification. We provide an extensive experimental comparison of the performance and properties of these competing classifiers on the challenging problem of protein interaction detection in biomedical publications. Our results show that GPs can match the performance of SVMs without the need for costly margin parameter tuning, whilst offering the advantage of an extendable probabilistic framework for text classification.