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dc.contributor.authorTriguero, Isaac
dc.contributor.authorGarcía López, Salvador 
dc.contributor.authorHerrera Triguero, Francisco
dc.identifier.citationPublisher version: I. Triguero, S. García and F. Herrera, "SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification," in IEEE Transactions on Cybernetics, vol. 45, no. 4, pp. 622-634, April 2015, [doi: 10.1109/TCYB.2014.2332003]es_ES
dc.descriptionThis work was supported by the Research Projects TIN2011-28488, P10-TIC-6858, and P11-TIC-7765. This paper was recommended by Associate Editor H. Wang.es_ES
dc.description.abstractSelf-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant proposals are currently inspired by boosting schemes to iteratively enlarge the labeled set. Despite their effectiveness, these methods are constrained by the number of labeled examples and their distribution, which in many cases is sparse and scattered. The aim of this paper is to design a framework, named synthetic examples generation for self-labeled semi-supervised classification, to improve the classification performance of any given self-labeled method by using synthetic labeled data. These are generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference. Next, these examples are incorporated in the main stages of the self-labeling process. The principal aspects of the proposed framework are: 1) introducing diversity to the multiple classifiers used by using more (new) labeled data; 2) fulfilling labeled data distribution with the aid of unlabeled data; and 3) being applicable to any kind of self-labeled method. In our empirical studies, we have applied this scheme to four recent self-labeled methods, testing their capabilities with a large number of data sets. We show that this framework significantly improves the classification capabilities of self-labeled techniques.es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectSelf-Labeled methodses_ES
dc.subjectSynthetic exampleses_ES
dc.subjectSemi-supervised classificationes_ES
dc.titleSEG-SSC: A Framework based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classificationes_ES

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