Analysis of presence-only data via semi-supervised learning approaches
AbstractPresence-only data occur in a classification, which consist of a sample of observations from the presence class and a large number of background observations with unknown presence/absence. Since absence data are generally unavailable, conventional semi-supervised learning approaches are no longer appropriate as they tend to degenerate and assign all observations to the presence class. In this article, we propose a generalized class balance constraint, which can be equipped with semi-supervised learning approaches to prevent them from degeneration. Furthermore, to circumvent the difficulty of model tuning with presence-only data, a selection criterion based on classification stability is developed, which measures the robustness of any given classification algorithm against the sampling randomness. The effectiveness of the proposed approach is demonstrated through a variety of simulated examples, along with an application to gene function prediction.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 59 (2013)
Issue (Month): C ()
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Web page: http://www.elsevier.com/locate/csda
Cross validation; Functional genomics; Stability; Support vector machine; Tuning;
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