{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:10:22Z","timestamp":1788844222330,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Identifying the small group of customers who hold a given policy in severely imbalanced tabular data is a recurring screening problem in insurance analytics. This study considers binary caravan-insurance policy-ownership classification on the COIL 2000 benchmark, where the positive-class prevalence is below 6%. The benchmark is a single cross-section, so the label describes current ownership rather than a future purchase event. We propose an Attention-based Symmetric AutoEncoder (ASAE) that combines an auxiliary symmetric reconstruction branch, a channel attention gate, and a marginal log-variance regularizer on a 32-dimensional latent representation. The regularizer operates on individual latent variances and is treated as a heuristic rather than as an estimator of joint differential entropy. Under a common tuning and evaluation protocol on a stratified partition, the ASAE is compared with five conventional machine learning methods and seven neural models. Across five paired runs, it achieves an F1-score of 0.6008 \u00b1 0.0115 and an area under the receiver operating characteristic curve (AUC) of 0.8584 \u00b1 0.0034. Relative to TabNet, the strongest baseline considered, the mean differences are 0.064 in F1-score (95% confidence interval 0.043\u20130.085) and 0.032 in AUC (95% confidence interval 0.017\u20130.048). The ordering is preserved across five stratified re-splits, four imbalance-handling configurations, and a complete type-consistent preprocessing rerun in which nominal attributes are one-hot encoded, oversampled with SMOTENC, and reconstructed with categorical cross-entropy losses (F1-score 0.6241 \u00b1 0.0074, AUC 0.8702 \u00b1 0.0050). All reported results use stratified random partitions of COIL 2000. Because 27% of the records share an identical predictor vector with another record, the official challenge separation and two grouped partitions are also defined, so that exact-duplicate and sociodemographic overlap can be isolated from the primary split. The training code, split indices, and per-run predictions used for the reported tables are publicly available.<\/jats:p>","DOI":"10.3390\/e28090985","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:13:25Z","timestamp":1788455605000},"page":"985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-Enhanced Autoencoder with Marginal-Variance-Regularized Feature Reconstruction for Imbalanced Insurance Policy-Ownership Classification"],"prefix":"10.3390","volume":"28","author":[{"given":"Jiaming","family":"Tian","sequence":"first","affiliation":[{"name":"School of Business, Soochow University, Suzhou 215222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyi","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Business, Soochow University, Suzhou 215222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Business, Soochow University, Suzhou 215222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2401-5197","authenticated-orcid":false,"given":"Xiao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering & Computer Science, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/B:MACH.0000035476.95130.99","article-title":"A Bias\u2013Variance Analysis of a Real World Learning Problem: The CoIL Challenge 2000","volume":"57","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Elkan, C. (2001). Magical Thinking in Data Mining: Lessons from CoIL Challenge 2000. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 26\u201329 August 2001, ACM.","DOI":"10.1145\/502512.502576"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, ACM.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_4","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30, Curran Associates."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","article-title":"Tabular Data: Deep Learning Is Not All You Need","volume":"81","author":"Armon","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_6","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A. (2021). Revisiting Deep Learning Models for Tabular Data. Advances in Neural Information Processing Systems 34, Curran Associates."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_8","first-page":"3371","article-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_9","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (2011). Contractive Auto-Encoders: Explicit Invariance During Feature Extraction. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, 28 June\u20132 July 2011, Omnipress."},{"key":"ref_10","unstructured":"Kingma, D.P., and Welling, M. (2014, January 14\u201316). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18\u201323 June 2018, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1017\/S174849952000024X","article-title":"AI in Actuarial Science\u2014A Review of Recent Advances\u2014Part 2","volume":"15","author":"Richman","year":"2021","journal-title":"Ann. Actuar. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"W\u00fcthrich, M.V., and Merz, M. (2023). Statistical Foundations of Actuarial Learning and Its Applications, Springer.","DOI":"10.1007\/978-3-031-12409-9"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7499","DOI":"10.1109\/TNNLS.2022.3229161","article-title":"Deep Neural Networks and Tabular Data: A Survey","volume":"35","author":"Borisov","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ar\u0131k, S.\u00d6., and Pfister, T. (2021). TabNet: Attentive Interpretable Tabular Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2\u20139 February 2021, AAAI Press.","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"ref_16","unstructured":"Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C.B., and Goldstein, T. (2021). SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. arXiv."},{"key":"ref_17","unstructured":"Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z. (2020). TabTransformer: Tabular Data Modeling Using Contextual Embeddings. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sakurada, M., and Yairi, T. (2014). Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction. Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, Gold Coast, QLD, Australia, 2 December 2014, ACM.","DOI":"10.1145\/2689746.2689747"},{"key":"ref_19","unstructured":"An, J., and Cho, S. (2015). Variational Autoencoder Based Anomaly Detection Using Reconstruction Probability, Seoul National University. Technical Report SNUDM-TR-2015-03."},{"key":"ref_20","unstructured":"Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H. (May, January 30). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. Proceedings of the 6th International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","article-title":"A Unifying Review of Deep and Shallow Anomaly Detection","volume":"109","author":"Ruff","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_22","first-page":"38","article-title":"Deep Learning for Anomaly Detection: A Review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_23","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems 30, Curran Associates."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany, 8\u201314 September 2018, Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_26","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014, January 14\u201316). Network in Network. Proceedings of the 2nd International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_27","unstructured":"Le, L., Patterson, A., and White, M. (2018). Supervised Autoencoders: Improving Generalization Performance with Unsupervised Regularizers. Advances in Neural Information Processing Systems 31, Curran Associates."},{"key":"ref_28","unstructured":"Ghosh, P., Sajjadi, M.S.M., Vergari, A., Black, M.J., and Sch\u00f6lkopf, B. (2020, January 26\u201330). From Variational to Deterministic Autoencoders. Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_29","unstructured":"Mondal, A.K., Singhal, L., Tiwary, P., AP, P., and Singla, P. (2023). Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25\u201327 April 2023, PMLR."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tian, M., Guo, D., Cui, Y., Pan, X., and Chen, S. (2021). Improving Auto-Encoder Novelty Detection Using Channel Attention and Entropy Minimization. Proceedings of the 2nd ACM International Conference on Multimedia in Asia, Virtual, 7\u20139 March 2021, ACM.","DOI":"10.1145\/3444685.3446311"},{"key":"ref_31","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021). Barlow Twins: Self-Supervised Learning via Redundancy Reduction. Proceedings of the 38th International Conference on Machine Learning, Virtual, 18\u201324 July 2021, PMLR."},{"key":"ref_32","unstructured":"Bardes, A., Ponce, J., and LeCun, Y. (2022, January 25\u201329). VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. Proceedings of the 10th International Conference on Learning Representations, Virtual."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 6\u201311 July 2015, PMLR."},{"key":"ref_35","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the 7th International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_39","unstructured":"Glorot, X., and Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 13\u201315 May 2010, PMLR."},{"key":"ref_40","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates."},{"key":"ref_41","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","first-page":"2579","article-title":"Visualizing Data Using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/9\/985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T04:25:29Z","timestamp":1788841529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/9\/985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["e28090985"],"URL":"https:\/\/doi.org\/10.3390\/e28090985","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]}}}