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An ensemble model for link prediction based on graph embedding
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2022 | Decision Support Systems | Citations: 0
Authors: Chen, Yen-Liang; Hsiao, Chen-Hsin; Wu, Chia-Chi

Abstract: A network is a form of data representation and is widely used in many fields. Fo ... Expand

Abstract: A network is a form of data representation and is widely used in many fields. For example, in social networks, we regard nodes as individuals or groups, and the edges between nodes are called links, that is, the interaction between people. By analyzing the interaction of nodes, we can learn more about network relationships. The core idea of link prediction is to predict whether there is a new relationship between a pair of nodes or to discover hidden links in the network. Link prediction has been applied to many fields such as social networking, ecommerce, bioinformatics, and so on. In addition, many studies have used graph embedding for link prediction, which effectively preserves the network structure and converts node information into a low-dimensional vector space. In this research, we used three graph embedding approaches: matrix decomposition based methods, random walk based methods, and deep learning based methods. Since each method has its own advantages and disadvantages, we propose an ensemble model to combine these graph embeddings into a new representation of each node. Then, we designed a two-stage link prediction model based on a multi-classifier ensemble and took the new node representation as its input. Performance evaluation was conducted on multiple data sets. Exper imental results show that the integration of multiple embedding methods and multiple classifiers can signifi cantly improve the performance of link prediction. Collapse

Semantic filters: deep learning stacking ensemble

Topics: electronic mail wiki social network Wikipedia

Methods: logistic regression machine learning deep learning support vector machine ensemble learning
COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking
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2021 | Information Systems Frontiers | Citations: 0
Authors: Elakkiya, R.; Vijayakumar, Pandi; Karuppiah, Marimuthu

Abstract: Infectious diseases are highly contagious due to rapid transmission and very cha ... Expand

Abstract: Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification. Collapse

Semantic filters: deep learning stacking ensemble

Topics: covid 19 spreadsheet open source online learning system

Methods: ensemble learning ImageNet meta learning machine learning computational algorithm

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An ensemble model for link prediction based on graph embedding

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking