scholarly journals A clustering and selection based transfer ensemble model for customer credit scoring

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4015-4026
Author(s):  
Jin Xiao ◽  
Ling Xie ◽  
Dunhu Liu ◽  
Yi Xiao ◽  
Yi Hu
Keyword(s):  
Transfer Learning ◽  
Ensemble Learning ◽  
Test Data ◽  
Credit Scoring ◽  
Experimental Results ◽  
Ensemble Model ◽  
Learning Models ◽  
Target Domain ◽  
Satisfactory Performance ◽  
Important Concern

Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines ensemble learning with transfer learning, and proposes a clustering and selection based transfer ensemble (CSTS) model to transfer the instances from related source domains to target domain for assisting in modeling. The experimental results in two customer credit scoring datasets show that CSTE model outperforms two traditional credit scoring models, as well as three existing transfer learning models.

scholarly journals Improving Human Happiness Analysis based on Transfer Learning：Algorithm Development and Validation (Preprint)

2021 ◽  
Author(s):  
Lele Yu ◽  
Shaowu Zhang ◽  
Yijia Zhang ◽  
Hongfei Lin
Keyword(s):  
Transfer Learning ◽  
Human Life ◽  
Language Model ◽  
Experimental Results ◽  
Target Domain ◽  
Detection Model ◽  
Human Happiness ◽  
Comparison Results ◽  
Semantically Enhanced ◽  
Voting Strategy

BACKGROUND Happiness refers to the joyful and pleasant emotions that humans produce subjectively. It is the positive part of emotions, and it affects the quality of human life. Therefore, understanding human happiness is a meaningful task in sentiment analysis. We mainly discuss two facets (Agency/Sociality) of happiness in this study. Through analysis and research on happiness, we can expand on new concepts that define happiness and enrich our understanding of emotions. OBJECTIVE In this paper, we treated each happy moment as a sequence of short sentences, then proposed a short happiness detection model based on transfer learning to analyze the Agency and Sociality aspects of happiness. METHODS Happiness analysis is a novel and challenging research task. However, the current dataset in the field of happiness is small. To solve this problem，we utilized the unlabeled training set and transfer learning to train a semantically enhanced language model in the target domain. Then, the trained language model with domain characteristics was further combined with other deep learning models to obtain various models. Finally, we used the improved voting strategy to further improve the experimental results. RESULTS The proposed approach was evaluated on the public dataset. Experimental results showed that our approach significantly outperforms the baselines. When predicting the Agency aspect of happiness, our approach achieved an accuracy of 0.8574 and an F1 score of 0.90, repectively. When predicting Sociality, our approach achieved an accuracy of 0.928 and an F1 score of 0.9360, respectively. CONCLUSIONS Through the evaluation of the dataset, the comparison results demonstrated the effectiveness of our approach for happiness analysis. Experimental results confirmed that our method achieved state-of-the-art performance and transfer learning effectively improved happiness analysis.

scholarly journals Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Nida Aslam ◽  
Irfan Ullah Khan ◽  
Farah Salem Alotaibi ◽  
Lama Abdulaziz Aldaej ◽  
Asma Khaled Aldubaikil
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Learning Model ◽  
Experimental Results ◽  
Ensemble Model ◽  
Fake News ◽  
Learning Models ◽  
Social Media Networks ◽  
Deep Learning Model

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

scholarly journals Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT

2021 ◽  
Author(s):  
Shubo Tian ◽  
Jinfeng Zhang
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Language Model ◽  
Ensemble Model ◽  
Test Results ◽  
Learning Models ◽  
Baseline Model ◽  
Satisfactory Performance ◽  
The Mean ◽  
Better Than

The BioCreative VII Track 5 calls for participants to tackle the multi-label classification task for automated topic annotation of COVID-19 literature. In our participation, we evaluated several deep learning models built on PubMedBERT, a pre-trained language model, with different strategies addressing the challenges of the task. Specifically, multi-instance learning was used to deal with the large variation in the lengths of the articles, and focal loss function was used to address the imbalance in the distribution of different topics. We found that the ensemble model performed the best among all the models we have tested. Test results of our submissions showed that our approach was able to achieve satisfactory performance with an F1 score of 0.9247, which is significantly better than the baseline model (F1 score: 0.8678) and the mean of all the submissions (F1 score: 0.8931).

scholarly journals Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ahmed I. Iskanderani ◽  
Ibrahim M. Mehedi ◽  
Abdulah Jeza Aljohani ◽  
Mohammad Shorfuzzaman ◽  
Farzana Akther ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Transfer Learning ◽  
Ensemble Model ◽  
Learning Models ◽  
X Ray ◽  
Proposed Model ◽  
Cloud Server ◽  
Medical Sensors ◽  
Chest X Ray

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

scholarly journals Transfer Learning for Collaborative Filtering Using a Psychometrics Model

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Haijun Zhang ◽  
Bo Zhang ◽  
Zhoujun Li ◽  
Guicheng Shen ◽  
Liping Tian
Keyword(s):  
Collaborative Filtering ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Experimental Results ◽  
Extended Model ◽  
Target Domain ◽  
Rating Data ◽  
Source Domain ◽  
One Dimensional ◽  
Recommendation Algorithms

In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

2021 ◽  
Vol 11 (9) ◽  
pp. 3782
Author(s):  
Chu-Hui Lee ◽  
Chen-Wei Lin
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Method ◽  
Phase Transfer ◽  
Recognition Task ◽  
Phase Detection ◽  
Target Domain ◽  
Two Phase ◽  
Detection Technology ◽  
Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

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