Semi-supervised learning based on nearest neighbor rule and cut edges

Wang, Yu; Xu, Xiaoyan; Zhao, Haifeng; Hua, Zhongsheng

doi:10.1016/j.knosys.2010.03.012

Cited by 68 publications

(22 citation statements)

References 17 publications

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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%

“…Using the recommendation established in [41], in the division process we do not maintain the class proportion in the labeled and unlabeled sets since the main aim of SSC is to exploit unlabeled data for better classification results. Hence, we use a random selection of examples that will be marked as labeled instances, and the class label of the rest of the instances will be removed.…”

Section: A Data Sets and Parametersmentioning

confidence: 99%

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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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Section: B Self-labeled Techniques: Previous Workmentioning

confidence: 99%

Section: A Data Sets and Parametersmentioning

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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“…But this method can cause several problems like misclassification, much noise in labeled data and also cause error reinforcement. Y.Wang et.al proposed a method to solve this problem and is called self training nearest neighbour rule using cut edges [2] .This method is to pool both testing samples and training sample in an iterative way. There are two aspects .…”

Section: 3self Training Nearest Neighbour Rule Using Cut Edgesmentioning

confidence: 99%

Survey on Semi Supervised Classification Methods and Feature Selection

Innocent¹,

Kurian

2013

IJRET

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“…Although some techniques, e.g. data editing [5], have been employed to alleviate this noise-related problem [6], results are yet undesirable. As a result, it could always suffer from introducing too much wrongly labeled candidates to the labeled training set, which may severely degrades performance.…”

Section: Introductionmentioning

confidence: 99%

Using Unlabeled Data to Improve Inductive Models by Incorporating Transductive Models

Cheng¹,

Liu²,

Tang³

2014

IJARAI

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Abstract-This paper shows how to use labeled and unlabeled data to improve inductive models with the help of transductive models. We proposed a solution for the self-training scenario. Selftraining is an effective semi-supervised wrapper method which can generalize any type of supervised inductive model to the semi-supervised settings. it iteratively refines a inductive model by bootstrap from unlabeled data. Standard self-training uses the classifier model(trained on labeled examples) to label and select candidates from the unlabeled training set, which may be problematic since the initial classifier may not be able to provide highly confident predictions as labeled training data is always rare. As a result, it could always suffer from introducing too much wrongly labeled candidates to the labeled training set, which may severely degrades performance. To tackle this problem, we propose a novel self-training style algorithm which incorporate a graph-based transductive model in the self-labeling process. Unlike standard self-training, our algorithm utilizes labeled and unlabeled data as a whole to label and select unlabeled examples for training set augmentation. A robust transductive model based on graph markov random walk is proposed, which exploits manifold assumption to output reliable predictions on unlabeled data using noisy labeled examples. The proposed algorithm can greatly minimize the risk of performance degradation due to accumulated noise in the training set. Experiments show that the proposed algorithm can effectively utilize unlabeled data to improve classification performance.

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Semi-supervised learning based on nearest neighbor rule and cut edges

Cited by 68 publications

References 17 publications

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

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

Survey on Semi Supervised Classification Methods and Feature Selection

Using Unlabeled Data to Improve Inductive Models by Incorporating Transductive Models

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