 Analysis of presence-only data via semi-supervised learning approachesJunhui Wang and Yixin Fang Computational Statistics & Data Analysis, 2013, vol. 59, issue C, 134-143 Abstract: Presence-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. Keywords: Cross validation; Functional genomics; Stability; Support vector machine; Tuning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:59:y:2013:i:c:p:134-143 DOI: 10.1016/j.csda.2012.10.007 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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