Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Fazakis, Nikos; Kanas, Vasileios G.; Aridas, Christos K.; Karlos, Stamatis; Kotsiantis, Sotiris

doi:10.3390/e21100988

Cited by 17 publications

(12 citation statements)

References 62 publications

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“…In order to validate our proposed framework, we also present comparisons with a recent state-of-the-art active semi-supervised framework [ 35 ], considering the RF classifier for other biological datasets, such as Haberman, Heart Statlog and Lymphograph [ 54 ] (see Table 9 ). Our framework achieves higher accuracies and requires fewer (much less than 10% of the dataset of) labeled training samples compared to the state of the art one, considering all datasets and active learning strategies.…”

Section: Resultsmentioning

confidence: 99%

“…Besides that, in their experiments, the authors presented comparisons between only logistic approaches. Another work [ 35 ] employs a self-training method in which the entropy of unlabeled samples is used in the active learning process, while the semi-supervised learning uses the probability distribution of all possible labels for the samples. Other combinations of active learning and semi-supervised techniques have been proposed in the literature and somewhat successful when applied to distinct contexts, such as: face recognition [ 36 ], diagnosis of intestinal parasites in humans [ 30 ], extraction of protein interaction sentences [ 37 ], unknown and label-scarce classes [ 38 ], sound classification [ 39 ], intrusion detection system [ 40 , 41 ] and textual classification [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Active semi-supervised learning for biological data classification

2020

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Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semisupervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active semi-supervised learning for biological data classification

2020

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“…It is well known that the configurations of the training set and testing set play important roles in the assessment of the LCZ classification [28,45,59]. Since the ground truth data was usually limited, 10 labeled samples were randomly selected in each class for simulating the insufficiency of labeled samples and the remaining samples were used as a testing set, which is widely used in many semi-supervised researches [60,61]. Also, the final experimental results were the average performance of 10 run outcomes for providing better representativeness.…”

Section: Experimental Descriptionmentioning

confidence: 99%

Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones

Zhao

Zhong

et al. 2019

Remote Sensing

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Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isolated areas in the LCZ maps. In this paper, a spatial-contextual information-based self-training classification framework (SCSF) is proposed to solve this LCZ classification problem. In SCSF, conditional random field (CRF) is used to integrate the classification and spatial smoothing processing into one model and a self-training method is adopted, considering that the lack of sufficient expert-labeled training samples is always a big issue, especially for the complex LCZ scheme. Moreover, in the unary potentials of CRF modeling, pseudo-label selection using a self-training process is used to train the classifier, which fuses the regional spatial information through segmentation and the local neighborhood information through moving windows to provide a more reliable probabilistic classification map. In the pairwise potential function, SCSF can effectively improve the classification accuracy by integrating the spatial-contextual information through CRF. The experimental results prove that the proposed framework is efficient when compared to the traditional mapping product of WUDAPT in LCZ classification.

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“…Semisupervised learning is a technique that utilizes unlabeled data to improve the label efficiency. Combining AL with semisupervised learning can increase the label efficiency further [ 16 ]. Graph neural network (GNN) models have achieved state-of-the-art performance in node classification [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Active Learning for Node Classification: An Evaluation

Madhawa

Murata

2020

Entropy

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Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being used as a solution to train classification models with less labeled instances by selecting only the most informative instances for labeling. This is especially important when the labeled data are scarce or the labeling process is expensive. In this paper, we study the application of active learning on attributed graphs. In this setting, the data instances are represented as nodes of an attributed graph. Graph neural networks achieve the current state-of-the-art classification performance on attributed graphs. The performance of graph neural networks relies on the careful tuning of their hyperparameters, usually performed using a validation set, an additional set of labeled instances. In label scarce problems, it is realistic to use all labeled instances for training the model. In this setting, we perform a fair comparison of the existing active learning algorithms proposed for graph neural networks as well as other data types such as images and text. With empirical results, we demonstrate that state-of-the-art active learning algorithms designed for other data types do not perform well on graph-structured data. We study the problem within the framework of the exploration-vs.-exploitation trade-off and propose a new count-based exploration term. With empirical evidence on multiple benchmark graphs, we highlight the importance of complementing uncertainty-based active learning models with an exploration term.

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Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Cited by 17 publications

References 62 publications

Active semi-supervised learning for biological data classification

Active semi-supervised learning for biological data classification

Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones

Active Learning for Node Classification: An Evaluation

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