A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

Wang, Chongren; Han, Demin; Liu, Qigang; Luo, Suyuan

doi:10.1109/access.2018.2887138

Cited by 104 publications

(56 citation statements)

References 12 publications

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“…Another factor we use is the attention mechanism. In recent years, attention has emerged out as a widely used and important tool in field of deep learning . Attention can be defined as a vector derived as output of dense layer of network using the softmax function.…”

Section: Our Methodsmentioning

confidence: 99%

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Fake news detection using deep learning models: A novel approach

Kumar

Asthana

Upadhyay

et al. 2019

Trans Emerging Tel Tech

131

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With the ever increase in social media usage, it has become necessary to combat the spread of false information and decrease the reliance of information retrieval from such sources. Social platforms are under constant pressure to come up with efficient methods to solve this problem because users' interaction with fake and unreliable news leads to its spread at an individual level. This spreading of misinformation adversely affects the perception about an important activity, and as such, it needs to be dealt with using a modern approach. In this paper, we collect 1356 news instances from various users via Twitter and media sources such as PolitiFact and create several datasets for the real and the fake news stories. Our study compares multiple state‐of‐the‐art approaches such as convolutional neural networks (CNNs), long short‐term memories (LSTMs), ensemble methods, and attention mechanisms. We conclude that CNN + bidirectional LSTM ensembled network with attention mechanism achieved the highest accuracy of 88.78%, whereas Ko et al tackled the fake news identification problem and achieved a detection rate of 85%.

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Section: Our Methodsmentioning

confidence: 99%

“…In recent years, attention has emerged out as a widely used and important tool in field of deep learning. [25][26][27][28] Attention can be defined as a vector derived as output of dense layer of network using the softmax function. Before attention, one needed to compress all the input information into a fixed length vector.…”

Section: Model Preparationmentioning

confidence: 99%

Fake news detection using deep learning models: A novel approach

Kumar

Asthana

Upadhyay

et al. 2019

Trans Emerging Tel Tech

131

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show abstract

“…These methods solve problems such as increased data size and unbalanced data structure from different angles, and greatly promoted the development of personal credit assessment. According to the current research, GBDT and NN are particularly outstanding in the field of credit scoring due to their good performance [2]- [5], but they also have weaknesses.…”

Section: Related Workmentioning

confidence: 99%

“…OICSM integrates gradient boosting decision tree (GBDT) and neural network (NN) to make the credit scoring model has online training and update capabilities, and can handle multiple types of features. GBDT has a good performance in learning over dense numerical data [2], [4] and NN method is better at learning over sparse categorical data [3], [5]. The proposed OICSM can effectively process dense numerical features and sparse categorical features at the same time.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Online Credit Scoring Model for P2P Lending

2020

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Credit scoring models have been widely used in traditional financial institutions for many years. Using these models in P2P Lending have limitations. First, the credit data of P2P usually contains dense numerical features and sparse categorical features. Second, the existing credit scoring models are generally cannot be updated online. The loan transaction of P2P lending is very frequent and the new data leads data distribution to change. A credit scoring model without considering data update causes a serious deviation or even failure in subsequent credit assessment. In this paper, we propose a new online integrated credit scoring model (OICSM) for P2P Lending. OICSM integrates gradient boosting decision tree and neural network to make the credit scoring model can handle two types of features more effectively and update online. Offline and online experiments based on real and representative credit datasets are conducted to verify the effectiveness and superiority of the proposed model. Experimental results demonstrate that OICSM can significantly improve performance due to its advantage in deep learning over two features, and it can further correct model deterioration due to its online dynamic update capability.

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“…Attention mechanism can handle this problem well [22]. is mechanism has been widely used in natural language processing (NLP) [23] and image processing [24]. More recently, Oktay et al [25] integrated the attention gate (AG) model to classical U-Net architecture to highlight salient features.…”

Section: Related Studiesmentioning

confidence: 99%

Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

Huang

Zhu

2020

Mathematical Problems in Engineering

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Accuracy segmentation of the nuclei and cytoplasm in Pap smear images is challenging in cervix cytological analysis. In this paper, a new fusion algorithm combining the asymmetric generalized Gaussian and Cauchy mixture model (GGCMM) with a shape constraint level set method to segment overlapping cervical smear cells is put forward. The proposed approach starts by separating nuclei and cytoplasm cluster through asymmetric GGCMM, where each component is a mixture of generalized Gaussian distribution and Cauchy distribution. The proposed asymmetric GGCMM takes into account the asymmetry of generalized Gaussian distribution and the heavier tail of Cauchy distribution. New probability distribution fits different shapes of observed data more flexibly. Then, we apply the morphological operation to remove fake nuclei which is usually much smaller than real nuclei. After that, the improved level set energy function with a distance map and a new shape prior term are applied to extract the contours of overlapping cervical cells. Due to this new level set energy function, the segmentation of every individual cell worked well, especially in overlapping areas. We evaluate the proposed method by using the ISBI 2014 Challenge Dataset. The results demonstrate that our approach outperforms existing methods in extracting overlapping cervical cells and obtains accurate cell contours.

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A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

Cited by 104 publications

References 12 publications

Fake news detection using deep learning models: A novel approach

Fake news detection using deep learning models: A novel approach

A Deep Learning Based Online Credit Scoring Model for P2P Lending

Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

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