Pre-service mathematics teachers’ problem-formulation processes: Development of the revised active learning framework

2022 ◽  
Vol 65 ◽  
pp. 100918
Author(s):  
Beyzanur Erkan ◽  
Tuğrul Kar
Keyword(s):  
Active Learning ◽  
Mathematics Teachers ◽  
Problem Formulation ◽  
Learning Framework

scholarly journals Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 78
Author(s):  
Jianhua Cao ◽  
Tao Liu ◽  
Jianjun Chen ◽  
Tao Yang ◽  
Xiuxiu Zhu ◽  
...  
Keyword(s):  
Active Learning ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Effective Means ◽  
Learning Paradigm ◽  
Class Label ◽  
Learning Framework ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

scholarly journals HEAL: Heterogeneous Ensemble and Active Learning Framework

2021 ◽  
Author(s):  
Anubhav Chhabra ◽  
Tirumala Sree Akhil Nandyala ◽  
Paula Branco
Keyword(s):  
Active Learning ◽  
Learning Framework ◽  
Heterogeneous Ensemble

scholarly journals Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0188996 ◽  
Author(s):  
Muhammad Ahmad ◽  
Stanislav Protasov ◽  
Adil Mehmood Khan ◽  
Rasheed Hussain ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Image Classification ◽  
Learning Framework

scholarly journals A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

2020 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Nasehe Jamshidpour ◽  
Abdolreza Safari ◽  
Saeid Homayouni
Keyword(s):  
Active Learning ◽  
Supervised Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Accurate Estimation ◽  
Data Sets ◽  
Learning Framework ◽  
Spatial Graph ◽  
Active Learning Method ◽  
Significant Superiority

This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations.

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