SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification

Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

Abstract

Self-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 work is to design a framework, named SEG-SSC, 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: (a) introducing diversity to the multiple classifiers used by using more (new) labeled data, (b) fulfilling labeled data distribution with the aid of unlabeled data, and (c) 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.

Citation

Triguero, I., Garcia, S., & Herrera, F. (2015). SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification. IEEE Transactions on Cybernetics, 45(4), https://doi.org/10.1109/TCYB.2014.2332003

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2014
• Online Publication Date: Jul 1, 2014
• Publication Date: Apr 30, 2015
• Deposit Date: Sep 4, 2017
• Publicly Available Date: Sep 4, 2017
• Journal: IEEE Transactions on Cybernetics
• Electronic ISSN: 2168-2275
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 4
• DOI: https://doi.org/10.1109/TCYB.2014.2332003
• Keywords: Prototypes, Training, Reliability, Prediction algorithms, Cybernetics, Manifolds, Standards
• Public URL: https://nottingham-repository.worktribe.com/output/748937
• Publisher URL: http://ieeexplore.ieee.org/document/6847198/
• Additional Information: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Sep 4, 2017