Co-training with relevant random subspaces

Yaslan, Yusuf; Çataltepe, Zehra

doi:10.1016/j.neucom.2010.01.018

Cited by 78 publications

(30 citation statements)

References 23 publications

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“…Random Subspaces have also been successfully used for semi-supervised learning. Yaslan and Cataltepe [39] used randomized set of features for cotraining an ensemble of classifiers, while Yu et al [40] used them for a semi-supervised dimensional reduction using graphs. Recent work by Carbonneau et al [9] proved that this method offers very good performance in multi-instance learning.…”

Section: Related Workmentioning

confidence: 99%

The deterministic subspace method for constructing classifier ensembles

Koziarski

Krawczyk

Woźniak

2017

Pattern Anal Applic

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Section: Related Workmentioning

confidence: 99%

The deterministic subspace method for constructing classifier ensembles

Koziarski

Krawczyk

Woźniak

2017

Pattern Anal Applic

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“…A further similar approach is Co-Bagging [37], [38] where confidence is estimated from the local accuracy of committee members. Other recent self-labeled approaches are [39], [40], [41], [42], [43].…”

Section: B Self-labeled Techniques: Previous Workmentioning

confidence: 99%

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Triguero

García

Herrera

2015

IEEE Trans. Cybern.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. 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.

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“…The proposed alternatives are generally ensemblebased and differ on the strategy they used to generate component classifiers. Methods for constructing ensembles include manipulation of the training samples by resampling (bootstrap aggregation or bagging) [15], [16], [17], [18], [19], [20] or using random subspaces [15], [19], [21], [22].…”

Section: B Semi-supervised Ensemble Learningmentioning

confidence: 99%

Semi-supervised Feature Importance Evaluation with Ensemble Learning

Barkia

Elghazel

Aussem

2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-We consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in high dimensional datasets, when only a small set of labeled examples is available. We propose a new semi-supervised feature importance evaluation method (SSFI for short), that combines ideas from co-training and random forests with a new permutation-based out-of-bag feature importance measure. We provide empirical results on several benchmark datasets indicating that SSFI can lead to significant improvement over state-of-the-art semi-supervised and supervised algorithms.

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Co-training with relevant random subspaces

Cited by 78 publications

References 23 publications

The deterministic subspace method for constructing classifier ensembles

The deterministic subspace method for constructing classifier ensembles

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

Semi-supervised Feature Importance Evaluation with Ensemble Learning

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