Combining Active Learning with Self-train algorithm for classification of multimodal problems

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

doi:10.1109/iisa.2019.8900724

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…In addition, the ETHOS can be combined with various similar HS datasets-as we stated here initially with two different data collections-for evaluation reasons. The development of hybrid weakly supervised HS detection models, merging semi-supervised and active learning strategies under common frameworks, alleviating human intervention based on decisions over the gathered unlabelled instances that come solely from the side of a robust learner [23,60], constitutes another very promising ambition. Online HS detection and prevention tools, such as Hatebusters among others, are highly favoured by such approaches.…”

Section: Discussionmentioning

confidence: 99%

ETHOS: a multi-label hate speech detection dataset

Mollas

Chrysopoulou

Karlos

et al. 2022

Complex Intell. Syst.

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Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

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Section: Discussionmentioning

confidence: 99%

ETHOS: a multi-label hate speech detection dataset

Mollas

Chrysopoulou

Karlos

et al. 2022

Complex Intell. Syst.

Self Cite

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“…The use of metrics of informativeness that are popular in the field of AL could boost the predictive performance of SSL methods and vice versa, as the authors of [60] demonstrated, studying the exploitation of centrality measures that stem from graph-based representation of data for capturing data heterogeneity. Use of AL + SSL based on ensemble learners either with UncS or with more targeted query strategies could boost the overall performance on classification tasks without demanding much effort from human annotators or reducing expenses induced by the corresponding crowdsourcing services [14]. The aspect of applying query strategies that avoid using uncertainty-based directions but prefer guidance by interactions among the decisions of multiple learners seems really promising, either for obtaining decisions through distinct iterations of Logitboost-based classifiers or for blending this powerful classifier into a pool of available classification algorithms [61].…”

Section: Discussionmentioning

confidence: 99%

“…Since the need for well-established predictions during the labeling stage of u i is one of the most crucial point during AL strategies, the exploitation of ensemble learners seems mandatory in order to capture better the insights of the examined data. Several recent works are also directed towards introducing ensemble learners into AL or other WSL variants, such as Semi-supervised Learning (SSL) [11], Cooperative Learning (CL) [12]-also known as AL + SSL [13,14]-or even Transfer Learning (TL) [15], while the field of supervised ensemble learners is still in bloom [16].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

2020

Self Cite

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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.

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“…This study focuses on showing the difference between active learning, selflearning, and random sampling. In the literature, many studies combine self and active learning methods to obtain better results [16].…”

Section: Recent Studies On Semi-supervised Learning and Active Learningmentioning

confidence: 99%

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

Kiranoglu¹,

Tuysuzoglu²,

Kiyak³

2021

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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In line with technological developments, machine learning/data mining studies have significantly scaled up in crime analysis. The prediction of crime occurrences, the detection of the spatial/temporal distribution of the criminal cases, forecasting the type of crime are some of these study areas. By taking crime data resulting from a substantial increase in crime rates into consideration, unlabeled data can be utilized to enhance exploring the patterns of crime for future events or to make crime-related predictions easily. Therefore, in this study, active learning, selflearning, and random sampling techniques are applied to predict the outcome of criminal searches in England using the police data of 2019. According to the experimental analysis, active learning outperforms its counterparts using its entropy-based smart selection strategy data in case there is little labeled data.

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Combining Active Learning with Self-train algorithm for classification of multimodal problems

Cited by 10 publications

References 34 publications

ETHOS: a multi-label hate speech detection dataset

ETHOS: a multi-label hate speech detection dataset

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

Prediction of Crime Occurrence in case of Scarcity of Labeled Data

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