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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: 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: stacking ensemble

Topics: covid 19 spreadsheet open source online learning system

Methods: ensemble learning ImageNet meta learning machine learning computational algorithm
Two-stage consumer credit risk modelling using heterogeneous ensemble learning
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2019 | Decision Support Systems | Citations: 0
Authors: Papouskova, Monika; Hajek, Petr

Abstract: Modelling consumer credit risk is a crucial task for banks and non-bank financia ... Expand

Abstract: Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. To model the overall credit risk of a consumer loan in terms of expected loss (EL), three key credit risk parameters must be estimated: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Research to date has tended to model these parameters separately. Moreover, a neglected area in the field of LGD/EAD modelling is the application of ensemble learning, which by benefitting from diverse base learners reduces the over-fitting problem and enables modelling diverse risk profiles of defaulted loans. To overcome these problems, this paper proposes a two-stage credit risk model that integrates (1) class-imbalanced ensemble learning for predicting PD (credit scoring), and (2) an EAD prediction using a regression ensemble. Furthermore, multi-objective evolutionary feature selection is used to minimize both the misclassification cost (root mean squared error) of the PD and EAD models and the number of attributes necessary for modelling. For this task, we propose a misclassification cost metric suitable for consumer loans with fixed exposure because it combines opportunity cost and LGD. We show that the proposed credit risk model is not only more effective than single-stage credit risk models but also outperforms state-of-the-art methods used to model credit risk in terms of prediction and economic performance. Collapse

Semantic filters: stacking ensemble

Topics: peer-to-peer model enterprise information system missing data

Methods: computational algorithm ensemble learning meta learning machine learning support vector machine
Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning
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2018 | Journal of Management Information Systems | Citations: 102
Authors: Kumar, Naveen; Venugopal, Deepak; Qiu, Liangfei; Kumar, Subodha

Abstract: Opinion spammers exploit consumer trust by posting false or deceptive reviews th ... Expand

Abstract: Opinion spammers exploit consumer trust by posting false or deceptive reviews that may have a negative impact on both consumers and businesses. These dishonest posts are difficult to detect because of complex interactions between several user characteristics, such as review velocity, volume, and variety. We propose a novel hierarchical supervised-learning approach to increase the likelihood of detecting anomalies by analyzing several user features and then characterizing their collective behavior in a unified manner. Specifically, we model user characteristics and interactions among them as univariate and multivariate distributions. We then stack these distributions using several supervised-learning techniques, such as logistic regression, support vector machine, and k-nearest neighbors yielding robust meta-classifiers. We perform a detailed evaluation of methods and then develop empirical insights. This approach is of interest to online business platforms because it can help reduce false reviews and increase consumer confidence in the credibility of their online information. Our study contributes to the literature by incorporating distributional aspects of features in machine-learning techniques, which can improve the performance of fake reviewer detection on digital platforms. Collapse

Semantic filters: stacking ensemble

Topics: online review email spam digital platform deception detection fake review

Methods: machine learning product review supervised learning logistic regression support vector machine
Using Big Data for Predicting Freshman Retention

2015 | International Conference on Information Systems | Citations: 0
Authors: Ram, Sudha; Wang, Yun; Currim, Faiz; Currim, Sabah

Abstract: Traditional research in student retention is survey-based, relying on data colle ... Expand

Abstract: Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall. Collapse

Semantic filters: stacking ensemble

Topics: smart card big data social network electronic mail telephone

Methods: computational algorithm machine learning random forest classification ensemble learning experiment
Metafraud: A Meta-Learning Framework for Detecting Financial Fraud

2012 | Management Information Systems Quarterly | Citations: 0
Authors: Abbasi, Ahmed; Albrecht, Conan; Vance, Anthony; Hansen, James

Abstract: Financial fraud can have serious ramifications for the long-term sustainability ... Expand

Abstract: Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators. Collapse

Semantic filters: stacking ensemble

Topics: fraud detection organizational context business intelligence individual impact legal context

Methods: machine learning meta learning experiment support vector machine semi-supervised learning

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Found 6 articles

An ensemble model for link prediction based on graph embedding

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

Two-stage consumer credit risk modelling using heterogeneous ensemble learning

Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning

Using Big Data for Predicting Freshman Retention

Metafraud: A Meta-Learning Framework for Detecting Financial Fraud