Abstract
With the development of social media sites, user credit grading, which served as an important and fashionable problem, has attracted substantial attention from a slew of developers and operators of mobile applications. In particular, multi-grades of user credit aimed to achieve (1) anomaly detection and risk early warning and (2) personalized information and service recommendation for privileged users. The above two goals still remained as up-to-date challenges. To these ends, in this article, we propose a novel regression-based method. Technically speaking, we define three natural ordered categories including BlockList, GeneralList, and AllowList according to users’ registration and behavior information, which preserve both the global hierarchical relationship of user credit and the local coincident features of users, and hence formulate user credit grading as the ordinal regression problem. Our method is inspired by KDLOR (kernel discriminant learning for ordinal regression), which is an effective and efficient model to solve ordinal regression by mapping high-dimension samples to the discriminant region with supervised conditions. However, the performance of KDLOR is fragile to the extreme imbalanced distribution of users. To address this problem, we propose a robust sampling model to balance distribution and avoid overfit or underfit learning, which induces the triplet metric constraint to obtain hard negative samples that well represent the latent ordered class information. A step further, another salient problem lies in ambiguous samples that are noises or located in the classification boundary to impede optimized mapping and embedding. To this problem, we improve sampling by identifying and evading noises in triplets to obtain hard negative samples to enhance robustness and effectiveness for ordinal regression. We organized training and testing datasets for user credit grading by selecting limited items from real-life huge tables of users in the mobile application, which are used in similar problems; moreover, we theoretically and empirically demonstrate the advantages of the proposed model over established datasets.
- R. Mori. 1992. System for storing history of use of programs including user credit data and having access by the proprietor: U.S. Patent 5,103,392[P]. 1992-4-7.Google Scholar
- Y. Chen, P. Ren, Yang Wang, and Maarten de Rijke. 2019. Bayesian personalized feature interaction selection for factorization machines. In 2019 International Conference on Research and Development in Information Retrieval (SIGIR'19). ACM, 665--674. Google Scholar
Digital Library
- T. Yuan et al. 2018. MS-UCF: A reliable recommendation method based on mood-sensitivity identification and user credit. In 2018 International Conference on Information Management and Processing (ICIMP’18). IEEE, 16–20.Google Scholar
- S. Kasower. 2017. Indirect monitoring and reporting of a user’s credit data: U.S. Patent Application 15/482,318[P]. 2017-9-28.Google Scholar
- W. A. Barnett and J. Liu. 2019. User cost of credit card services under risk with intertemporal nonseparability. Journal of Financial Stability 42 (2019), 18–35.Google Scholar
Cross Ref
- L. Wu, Yang Wang, and Ling Shao. 2019. Cycle-consistent deep generative hashing for cross-modal retrieval. IEEE Trans. Image Processing 28, 4 (2019), 1602–1612.Google Scholar
Digital Library
- Yang Wang, X. Lin, L. Wu, et al. 2015. Effective multi-query expansions: Robust landmark retrieval. In 2015 International Conference on Multimedia. ACM, 79--88. Google Scholar
Digital Library
- Y. Wang et al. 2017. Effective multi-query expansions: Collaborative deep networks for robust landmark retrieval. IEEE Trans. Image Processing 26, 3 (2017), 1393--1404. Google Scholar
Digital Library
- H. Fu, M. Gong, C. Wang, et al. 2018. Deep ordinal regression network for monocular depth estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2002–2011.Google Scholar
Cross Ref
- Z. Niu, M. Zhou, L. Wang, et al. 2016. Ordinal regression with multiple output CNN for age estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4920–4928.Google Scholar
Cross Ref
- M. DeYoreo and A. Kottas. 2018. Bayesian nonparametric modeling for multivariate ordinal regression. Journal of Computational and Graphical Statistics 27, 1 (2018), 71–84.Google Scholar
Cross Ref
- V. Torra, J. Domingo-Ferrer, J. M. Mateo-Sanz, et al. 2006. Regression for ordinal variables without underlying continuous variables. Information Sciences 176, 4 (2006), 465–474. Google Scholar
Digital Library
- E. Frank and M. Hall. 2001. A simple approach to ordinal classification. In European Conference on Machine Learning. Springer, Berlin, 145–156. Google Scholar
Digital Library
- B. Y. Sun, J. Li, D. D. Wu, et al. 2009. Kernel discriminant learning for ordinal regression. IEEE Transactions on Knowledge and Data Engineering 22, 6 (2009), 906–910. Google Scholar
Digital Library
- C. Drummond and R. C. Holte. C4. 5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II. Washington, DC: Citeseer, 11: 1–8.Google Scholar
- T. Zhu, Y. Lin, Y. Liu, et al. 2019. Minority oversampling for imbalanced ordinal regression. Knowledge-Based Systems 166 (2019), 140–155.Google Scholar
Cross Ref
- M. Pérez-Ortiz, P. A. Gutierrez, C. Hervás-Martínez, et al. 2014. Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Transactions on Knowledge and Data Engineering 27, 5 (2014), 1233–1245.Google Scholar
Cross Ref
- F. Schroff, D. Kalenichenko, and J. Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 815–823.Google Scholar
- G. Yang, X. Xu, and F. Zhao. 2019. Predicting user ratings with XGBoost algorithm. Data Analysis and Knowledge Discovery 3, 1 (2019), 118–126.Google Scholar
- M. Ala’raj and M. F. Abbod. 2016. Classifiers consensus system approach for credit scoring. Knowledge-Based Systems 104 (2016), 89–105. Google Scholar
Digital Library
- C. Luo, D. Wu, and D. Wu. 2017. A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence 65 (2017), 465–470. Google Scholar
Digital Library
- B. Jeong, J. Lee, and H. Cho. 2009. User credit-based collaborative filtering. Expert Systems with Applications 36, 3 (2009), 7309–7312. Google Scholar
Digital Library
- M. Ala’raj and M. F. Abbod. 2016. A new hybrid ensemble credit scoring model based on classifiers consensus system approach. Expert Systems with Applications 64 (2016), 36–55. Google Scholar
Digital Library
- A. Bequé and S. Lessmann. 2017. Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications 86 (2017), 42–53.Google Scholar
Cross Ref
- A. A. Taha and S. J. Malebary. 2020. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine [J]. IEEE Access 8 (2020), 25579--25587.Google Scholar
Cross Ref
- A. Bequé and S. Lessmann. 2017. Extreme learning machines for credit scoring An empirical evaluation. Expert Systems with Applications 86 (2017), 42–53.Google Scholar
Cross Ref
- F. Louzada, A. Ara, and G. B. Fernandes. 2016. Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science 21, 2 (2016), 117–134.Google Scholar
Cross Ref
- L. Polania, G. Fung, and D. Wang. 2019. Ordinal regression using noisy pairwise comparisons for body mass index range estimation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV’19). IEEE, 782–790.Google Scholar
- P. A. Gutierrez, M. Perez-Ortiz, J. Sanchez-Monedero, et al. 2015. Ordinal regression methods: Survey and experimental study. IEEE Transactions on Knowledge and Data Engineering 28, 1 (2015), 127–146. Google Scholar
Digital Library
- E. Frank, and M. Hall. 2001. A simple approach to ordinal classification. In European Conference on Machine Learning. Springer, Berlin, 145–156. Google Scholar
Digital Library
- W. Waegeman and L. Boullart. 2009. An ensemble of weighted support vector machines for ordinal regression. International Journal of Computer Systems Science and Engineering 3, 1 (2009), 47–51.Google Scholar
- W. Y. Deng, Q. H. Zheng, S. Lian, et al. 2010. Ordinal extreme learning machine. Neurocomputing 74, 1–3 (2010), 447–456. Google Scholar
Digital Library
- K. Kim and H. Ahn. 2012. A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research 39, 8 (2012), 1800–1811. Google Scholar
Digital Library
- J. Cheng, Z. Wang, and G. Pollastri. 2008. A neural network approach to ordinal regression. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 1279–1284.Google Scholar
- Y. S. Kwon, I. Han, and K. C. Lee. 1997. Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. Intelligent Systems in Accounting, Finance & Management 6, 1 (1997), 23–40.Google Scholar
Cross Ref
- Y. Wang, W. Zhang, L. Wu. 2016, Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. arXiv preprint arXiv:1608.05560. Google Scholar
Digital Library
- Yang Wang. 2020. Survey on deep multi-modal data analytics: Collaboration, rivalry and fusion. ACM Trans. Multimedia Computing, arXiv preprint arXiv:2006.08159.Google Scholar
- Y. Liu, K. C. C. Chan, et al. 2012. Neighborhood preserving ordinal regression. In Proceedings of the 4th International Conference on Internet Multimedia Computing and Service. 119–122. Google Scholar
Digital Library
- P. McCullagh. 1980. Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological) 42, 2 (1980), 109–127.Google Scholar
Cross Ref
- A. Agresti. 2003. Categorical Data Analysis. John Wiley & Sons.Google Scholar
- M. Mathieson. 1997. Ordered classes and incomplete examples in classification. In Advances in Neural Information Processing Systems. 550–556. Google Scholar
Digital Library
- S. Agarwal. 2008. Generalization bounds for some ordinal regression algorithms. In International Conference on Algorithmic Learning Theory. Springer, Berlin, 7–21. Google Scholar
Digital Library
- B. Zhao, F. Wang, and C. Zhang. 2009. Block-quantized support vector ordinal regression. IEEE transactions on Neural Networks 20, 5 (2009), 882–890. Google Scholar
Digital Library
- I. W. Tsang, J. T. Kwok, and P. M. Cheung. 2005. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research 6 (Apr, 2005), 363–392. Google Scholar
Digital Library
- B. Gu, V. S. Sheng, K. Y. Tay, et al. 2014. Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems 26, 7 (2014), 1403–1416.Google Scholar
Cross Ref
- C. Li, Q. Liu, J. Liu, et al. 2014. Ordinal distance metric learning for image ranking. IEEE Transactions on Neural Networks and Learning Systems 26, 7 (2014), 1551–1559.Google Scholar
Cross Ref
- R. Fathony, M. A. Bashiri, and B. Ziebart. 2017. Adversarial surrogate losses for ordinal regression. In Advances in Neural Information Processing Systems. 563–573. Google Scholar
Digital Library
- Y. Liu, Y. Liu, and K. C. C. Chan. 2011. Ordinal regression via manifold learning. In 25th AAAI Conference on Artificial Intelligence. Google Scholar
Digital Library
Index Terms
Robust Ordinal Regression: User Credit Grading with Triplet Loss-Based Sampling
Recommendations
Constructing and Combining Orthogonal Projection Vectors for Ordinal Regression
Ordinal regression is to predict categories of ordinal scale and it has wide applications in many domains where the human evaluation plays a major role. So far several algorithms have been proposed to tackle ordinal regression problems from a machine ...
Kernel Discriminant Learning for Ordinal Regression
Ordinal regression has wide applications in many domains where the human evaluation plays a major role. Most current ordinal regression methods are based on Support Vector Machines (SVM) and suffer from the problems of ignoring the global information of ...
Collaborative Filtering via Additive Ordinal Regression
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningAccurately predicting user preferences/ratings over items are crucial for many Internet applications, e.g., recommender systems, online advertising. In current main-stream algorithms regarding the rating prediction problem, discrete rating scores are ...






Comments