{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T04:01:18Z","timestamp":1749355278349,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665754","type":"print"},{"value":"9789819665761","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6576-1_4","type":"book-chapter","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:38:38Z","timestamp":1749274718000},"page":"42-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flexible-Order Feature-Interaction for\u00a0Mixed Continuous and\u00a0Discrete Variables with\u00a0Group-Level Interpretability"],"prefix":"10.1007","author":[{"given":"Zijie","family":"Zhai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchen","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Amekoe, K.M., Dilmi, M.D., Azzag, H., Dagdia, Z.C., Lebbah, M., Jaffre, G.: Tabsra: an attention based self-explainable model for tabular learning. In: ESANN (2023)","DOI":"10.14428\/esann\/2023.ES2023-37"},{"key":"4_CR2","unstructured":"Asuncion, A., Newman, D.: Uci machine learning repository (2007)"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: SIGKDD (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"4_CR5","unstructured":"Deng, H., Ren, Q., Zhang, H., Zhang, Q.: Discovering and explaining the representation bottleneck of dnns. In: ICLR (2022)"},{"key":"4_CR6","unstructured":"Enouen, J., Liu, Y.: Sparse interaction additive networks via feature interaction detection and sparse selection. In: NeurIPS (2022)"},{"key":"4_CR7","unstructured":"Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. In: NeurIPS (2022)"},{"key":"4_CR8","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data. In: NeurIPS (2021)"},{"key":"4_CR9","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? In: NeurIPS (2022)"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for ctr prediction. In: IJCAI (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"4_CR11","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: NeurIPS (2016)"},{"key":"4_CR12","unstructured":"Kleinbaum, D.G., Klein, M., Pryor, E.R.: Logistic regression: a self-learning text, vol.\u00a094. Springer (2002)"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Lerman, S., Venuto, C., Kautz, H., Xu, C.: Explaining local, global, and higher-order interactions in deep learning. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00126"},{"key":"4_CR14","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2018)"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dss.2014.03.001","volume":"62","author":"S Moro","year":"2014","unstructured":"Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22\u201331 (2014)","journal-title":"Decis. Support Syst."},{"key":"4_CR16","first-page":"145","volume":"1","author":"M Olave","year":"1989","unstructured":"Olave, M., Rajkovic, V., Bohanec, M.: An application for admission in public school systems. Expert Syst. Public Admin. 1, 145\u2013160 (1989)","journal-title":"Expert Syst. Public Admin."},{"key":"4_CR17","unstructured":"Pedregosa, F., et\u00a0al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"4_CR18","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: Catboost: unbiased boosting with categorical features. In: NeurIPS (2018)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Qiao, L., Wang, W., Lin, B.: Learning accurate and interpretable decision rule sets from neural networks. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i5.16555"},{"key":"4_CR20","unstructured":"Ren, J., Li, M., Liu, Z., Zhang, Q.: Interpreting and disentangling feature components of various complexity from dnns. In: ICML (2021)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: ICDM (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? explaining the predictions of any classifier. In: SIGKDD (2016)","DOI":"10.18653\/v1\/N16-3020"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press (2002)","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Song, W., et al.: Autoint: automatic feature interaction learning via self-attentive neural networks. In: CIKM (2019)","DOI":"10.1145\/3357384.3357925"},{"key":"4_CR26","unstructured":"Tsang, M., Cheng, D., Liu, Y.: Detecting statistical interactions from neural network weights. In: ICLR (2018)"},{"key":"4_CR27","unstructured":"Tsang, M., Rambhatla, S., Liu, Y.: How does this interaction affect me? interpretable attribution for feature interactions. In: NeurIPS (2020)"},{"key":"4_CR28","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Wang, R., Shivanna, R., Cheng, D., Jain, S., Lin, D., Hong, L., Chi, E.: Dcn v2: improved deep & cross network and practical lessons for web-scale learning to rank systems. In: WWW (2021)","DOI":"10.1145\/3442381.3450078"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, W., Liu, N., Wang, J.: Transparent classification with multilayer logical perceptrons and random binarization. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.6102"},{"issue":"2","key":"4_CR31","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1109\/TPAMI.2023.3328881","volume":"46","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Zhang, W., Liu, N., Wang, J.: Learning interpretable rules for scalable data representation and classification. IEEE Trans. Pattern Anal. Mach. Intell. 46(2), 1121\u20131133 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Yan, J., Chen, J., Wu, Y., Chen, D.Z., Wu, J.: T2g-former: organizing tabular features into relation graphs promotes heterogeneous feature interaction. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i9.26272"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6576-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:38:59Z","timestamp":1749274739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6576-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665754","9789819665761"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6576-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}