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A Multi-stage Self-adaptive Classifier Ensemble Model with Application in Credit Scoring
2019
IEEE Access
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INDEX TERMS Credit scoring, multi-stage, self-adaptive, classifier ensemble. ...
In the experimental study, three real-world credit datasets and four evaluation indicators are adopted for the performance evaluation. ...
The more layers the MCS produces, the higher dimensional data and more comprehensive features are obtained. In this study, two layers are used to verify the efficiency of the MCS. ...
doi:10.1109/access.2019.2922676
fatcat:uuw4vczo3rfpnnxgfilc7f4w4m
A novel end-to-end corporate credit rating model based on self-attention mechanism
2020
IEEE Access
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We propose a novel end-to-end architecture, the SMAGRU, based on self multi-head attention with gated recurrent neural networks, which is capable of evaluating a corporate credit rating by capturing the ...
Experiments compared with statistic and machine learning baseline models on different datasets show that our model contains superiority in multi-class credit rating classification. ...
RELATED WORKS
CORPORATE CREDIT RATING In commercial applications and economic studies, credit rating is mainly determined by subjective judgment and statistical-based models. ...
doi:10.1109/access.2020.3036469
fatcat:vznkoxlxqbcxrhacu72n4kxmhm
Social credit: a comprehensive literature review
2015
Financial Innovation
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Some interesting conclusions are summarized from the literature review. (1) Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social ...
The study considers the credit of various economic agents within the social credit system such as countries (or governments), corporations, and individuals and their credit variations in online markets ...
and NSFC No. 71301006), the National Program for Support of Top-Notch Young Professionals and the Fundamental Research Funds for the Central Universities in BUCT. ...
doi:10.1186/s40854-015-0005-6
fatcat:r4ackwjtgrapbohaowoq3optq4
An Enhanced Cascading Model for E-Commerce Consumer Credit Default Prediction
2021
Journal of Organizational and End User Computing
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The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. ...
This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. ...
(Luo & Chen, 2017) conducted the credit risk assessment and application of personal microfinance based on Logistic regression model. ...
doi:10.4018/joeuc.20211101.oa13
fatcat:ujt6ljotebcrhazha5d4usshei
Research on the Application of Machine Learning Algorithms in Credit Risk Assessment of Minor Enterprises
2021
Converter
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In order to understand the function of machine learning algorithms in predicting enterprise credit risk, the research designs five models, including Logistic Regression, Decision Tree, Naïve Bayesian, ...
Under the influence of COVID-19, minor enterprises, especially the manufacturing industry, are facing greater financial pressure and the possibility of non-performing loans is increasing. ...
Li et al. used multi-layer perceptron and radial basis functions to evaluate the credit of minor enterprises in the P2P online lending platform and multi-layer perceptron can better predict defaulting ...
doi:10.17762/converter.220
fatcat:miafkis7vzcwpjfe4kzym36wi4
Evaluation of Multi-Objective Optimizers for Cognitive Radio using Psychometric Methods
2012
Proceedings of the 7th International Conference on Cognitive Radio Oriented Wireless Networks
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A simulation study used unidimensional and multidimensional item response models to evaluate multi-objective cognitive engine optimizers based on two types of optimization algorithm: genetic algorithms ...
Item response models (IRMs) developed for use in fields such as education and psychology are applicable to cognitive radio testing due to parallels between cognitive radio and human cognition appear likely ...
ACKNOWLEDGMENT Thanks to Professors Tamal Bose, Tonya Smith-Jackson, and Kay Thamvichai for their help in making this work possible. ...
doi:10.4108/icst.crowncom.2012.248438
dblp:conf/crowncom/DietrichWV12
fatcat:asivwg5iobatfb4fb4jcw4gote
A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees
[article]
2020
arXiv
pre-print
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Successful machine learning methods can provide rapid analysis of credit scores while updating older ones on a daily time scale. ...
Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. ...
They studied German and Australian credit scoring data and conclude that multi-layer perceptron is not the most accurate neural network architecture and logistic regression outperforms all neural network ...
arXiv:2007.06617v1
fatcat:iz43gxrlnjdufetj4v6qddyy6u
Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example
2019
Open Journal of Business and Management
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This paper combines the nonlinear dimensionality reduction method, and the Restricted Boltzmann machine (RBM algorithm), to assess the credit risk of P2P borrowers. ...
credit risk assessment model based on RBM feature extraction combined with contrast divergence theory. ...
Acknowledgements The authors wish to acknowledge the valuable support of everyone who helped in the actual administration of the questionnaire and all the other participants of the study. ...
doi:10.4236/ojbm.2019.74107
fatcat:ezl4by6u35dd5npypdimuuga6a
Towards reliable named entity recognition in the biomedical domain
[article]
2019
bioRxiv
pre-print
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Results: We measure the effect that each strategy has when training/testing on the same corpus ("in-corpus" performance) and when training on one corpus and evaluating on another ("out-of-corpus" performance ...
The maximal increase we identified combines multi-task learning and variational dropout, which boosts out-of-corpus performance by 10.75%. ...
Acknowledgements This research was enabled in part by support provided by Compute Ontario (https://computeontario.ca/) and Compute Canada (www.computecanada.ca). ...
doi:10.1101/526244
fatcat:b2w22dcs4bezzjrtlyfwdrwllm
Multi-Head Attention: Collaborate Instead of Concatenate
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.16362v1 fatcat:t7hfj54czjfarirk27slx3ibo4
Collaborative multi-head attention reduces the size of the key and query projections by 4 for same accuracy and speed. Our code is public. ...
Our scheme decreases the number of parameters in an attention layer and can be used as a drop-in replacement in any transformer architecture. ...
applications. ...
arXiv:2006.16362v2
fatcat:pulug65azzhm7dbjnun2rcb2ke
C^3-index: A PageRank based multi-faceted metric for authors' performance measurement
[article]
2016
arXiv
pre-print
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In this paper, we propose C^3-index that combines the effect of citations and collaborations of an author in a systematic way using a weighted multi-layered network to rank authors. ...
We conduct our experiments on a massive publication dataset of Computer Science and show that - (i) C^3-index is consistent over time, which is one of the fundamental characteristics of a ranking metric ...
Other concise study on the topic was undertaken by Xu et al. [54] and Tscharntke et al. ...
arXiv:1610.07061v1
fatcat:bxq6se2bfre47dtkvihmxcklpa
Classification Credit Dataset Using Particle Swarm Optimization and Probabilistic Neural Network Models Based on the Dynamic Decay Learning Algorithm
2013
Automation Control and Intelligent Systems
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The result shows that this new hybrid algorithm outperforms the most common used algorithm such as multi-layer neural network. ...
This paper describes a credit risk evaluation system that uses supervised probabilistic neural network (PNN) models based on the Dynamic Decay learning algorithm (DDA). ...
Introduction and Related Work One another of first studies that became well-known in credit risk measurement was Z-score that is obtained from multi variable scoring model (Altman, 1968 ). ...
doi:10.11648/j.acis.20130105.12
fatcat:eh45mthtwnaynj3ia2kplq3fym
Multi-dimensional Evaluation Model of E-commerce Industrial Chain
2015
Open Automation and Control Systems Journal
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based on credit, from the dimensions of the identification of the mechanism of the e-commerce industrial chain, the comprehensive strength of enterprise, enterprise credit score, etc. ...
As for the serious impediment of credit to the rapid development of e-commerce cooperative management, this paper proposes the multi-dimensional evaluation model of the cooperative management of e-commerce ...
ACKNOWLEDGEMENTS Thanks to teacher of Hua Ying, Zhang XiaoHang and Wang Qi that they point out some of the valuable comments. ...
doi:10.2174/1874444301406010439
fatcat:o5bxiwnen5d3nekkfv6oydofcu
Multi-channel spectrograms for speech processing applications using deep learning methods
2020
Pattern Analysis and Applications
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In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. ...
and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to ...
Section 3 describes the two applications for multi-channel spectrograms considered in this study. ...
doi:10.1007/s10044-020-00921-5
fatcat:kpmij2ydnfei3nyhtixn6pkdea
Models for Prediction of Industrial Insolvency of Manufacturing Companies in India
2018
Asian Journal of Finance & Accounting
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like India.The results show that the forecasting ability of the models is higher than the empirical models already available such Altman's Z score model, Ohlson's model and model developed by Odom and ...
Investors, activists and corporations across the world are emphasizing on prediction of insolvency well in advance so that corrective actions can be taken and erosion of funds can be prevented. ...
A multi-layer feed forward network with an input layer, one hidden layer and output node is shown in the figure below. ...
doi:10.5296/ajfa.v10i2.13674
fatcat:y242immxlbdnlf4chdxrlirtwu
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