{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:34:35Z","timestamp":1743147275164,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031217425"},{"type":"electronic","value":"9783031217432"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-21743-2_3","type":"book-chapter","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:24:05Z","timestamp":1670509445000},"page":"27-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Classification with\u00a0Counterfactual Reasoning and\u00a0Active Learning"],"prefix":"10.1007","author":[{"given":"Azhar","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao","family":"Duong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thin","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"issue":"3","key":"3_CR1","first-page":"1031","volume":"120","author":"D Almond","year":"2005","unstructured":"Almond, D., Chay, K.Y., Lee, D.S.: The costs of low birth weight. Q. J. Econ. 120(3), 1031\u20131083 (2005)","journal-title":"Q. J. Econ."},{"issue":"2016","key":"3_CR2","first-page":"139","volume":"23","author":"J Angwin","year":"2016","unstructured":"Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias. ProPublica 23(2016), 139\u2013159 (2016)","journal-title":"ProPublica"},{"doi-asserted-by":"crossref","unstructured":"Apruzzese, G., Colajanni, M., Ferretti, L., Marchetti, M.: Addressing adversarial attacks against security systems based on machine learning. In: Proceedings of the International Conference on Cyber Conflict, vol. 900, pp. 1\u201318 (2019)","key":"3_CR3","DOI":"10.23919\/CYCON.2019.8756865"},{"doi-asserted-by":"publisher","unstructured":"Baird, H.S.: Document image defect models. In: Baird, H.S., Bunke, H., Yamamoto, K. (eds.) Structured Document Image Analysis, pp. 546\u2013556. Springer, Heidelberg (1992). https:\/\/doi.org\/10.1007\/978-3-642-77281-8_26","key":"3_CR4","DOI":"10.1007\/978-3-642-77281-8_26"},{"key":"3_CR5","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"issue":"11","key":"3_CR6","first-page":"3207","volume":"14","author":"L Bottou","year":"2013","unstructured":"Bottou, L., et al.: Counterfactual reasoning and learning systems: the example of computational advertising. J. Mach. Learn. Res. 14(11), 3207\u20133260 (2013)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Chang, C.H., Adam, G.A., Goldenberg, A.: Towards robust classification model by counterfactual and invariant data generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15212\u201315221 (2021)","key":"3_CR7","DOI":"10.1109\/CVPR46437.2021.01496"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1613\/jair.295","volume":"4","author":"DA Cohn","year":"1996","unstructured":"Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. J. Artif. Intell. Res. 4, 129\u2013145 (1996)","journal-title":"J. Artif. Intell. Res."},{"doi-asserted-by":"crossref","unstructured":"Collet, T., Pietquin, O.: Active learning for classification: an optimistic approach. In: Proceedings of the Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp. 1\u20138 (2014)","key":"3_CR10","DOI":"10.1109\/ADPRL.2014.7010610"},{"unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)","key":"3_CR11"},{"unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2019). https:\/\/archive.ics.uci.edu\/ml","key":"3_CR12"},{"unstructured":"Ducoffe, M., Precioso, F.: Adversarial active learning for deep networks: a margin based approach. arXiv preprint arXiv:1802.09841 (2018)","key":"3_CR13"},{"unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Data augmentation using synthetic data for time series classification with deep residual networks. arXiv preprint arXiv:1808.02455 (2018)","key":"3_CR14"},{"doi-asserted-by":"crossref","unstructured":"Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 329\u2013338 (2019)","key":"3_CR15","DOI":"10.1145\/3287560.3287589"},{"unstructured":"Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. In: Proceedings of the International Conference on Machine Learning. pp. 1183\u20131192 (2017)","key":"3_CR16"},{"doi-asserted-by":"crossref","unstructured":"Gong, C., Ren, T., Ye, M., Liu, Q.: Maxup: lightweight adversarial training with data augmentation improves neural network training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2474\u20132483 (2021)","key":"3_CR17","DOI":"10.1109\/CVPR46437.2021.00250"},{"unstructured":"Hern\u00e1ndez-Garc\u00eda, A., K\u00f6nig, P.: Data augmentation instead of explicit regularization. arXiv preprint arXiv:1806.03852 (2018)","key":"3_CR18"},{"doi-asserted-by":"crossref","unstructured":"Huang, W., Liu, H., Bowman, S.R.: Counterfactually-augmented SNLI training data does not yield better generalization than unaugmented data. arXiv preprint arXiv:2010.04762 (2020)","key":"3_CR19","DOI":"10.18653\/v1\/2020.insights-1.13"},{"doi-asserted-by":"crossref","unstructured":"Kamiran, F., Karim, A., Zhang, X.: Decision theory for discrimination-aware classification. In: Proceedings of the IEEE International Conference on Data Mining, pp. 924\u2013929 (2012)","key":"3_CR20","DOI":"10.1109\/ICDM.2012.45"},{"unstructured":"Kaushik, D., Hovy, E., Lipton, Z.C.: Learning the difference that makes a difference with counterfactually-augmented data. arXiv preprint arXiv:1909.12434 (2019)","key":"3_CR21"},{"doi-asserted-by":"crossref","unstructured":"Kumar, V.B., Kumar, S.S., Saboo, V.: Dermatological disease detection using image processing and machine learning. In: Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition, pp. 1\u20136 (2016)","key":"3_CR22","DOI":"10.1109\/ICAIPR.2016.7585217"},{"doi-asserted-by":"crossref","unstructured":"Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3\u201312 (1994)","key":"3_CR23","DOI":"10.1007\/978-1-4471-2099-5_1"},{"unstructured":"Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., Welling, M.: Causal effect inference with deep latent-variable models. arXiv preprint arXiv:1705.08821 (2017)","key":"3_CR24"},{"unstructured":"Maudslay, R.H., Gonen, H., Cotterell, R., Teufel, S.: It\u2019s all in the name: mitigating gender bias with name-based counterfactual data substitution. arXiv preprint arXiv:1909.00871 (2019)","key":"3_CR25"},{"doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 607\u2013617 (2020)","key":"3_CR26","DOI":"10.1145\/3351095.3372850"},{"doi-asserted-by":"crossref","unstructured":"Neal, L., Olson, M., Fern, X., Wong, W.K., Li, F.: Open set learning with counterfactual images. In: Proceedings of the European Conference on Computer Vision, pp. 613\u2013628 (2018)","key":"3_CR27","DOI":"10.1007\/978-3-030-01231-1_38"},{"doi-asserted-by":"crossref","unstructured":"Nguyen, D., Gupta, S., Rana, S., Shilton, A., Venkatesh, S.: Bayesian optimization for categorical and category-specific continuous inputs. In: AAAI. vol. 34, pp. 5256\u20135263 (2020)","key":"3_CR28","DOI":"10.1609\/aaai.v34i04.5971"},{"key":"3_CR29","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.ins.2021.06.095","volume":"576","author":"D Nguyen","year":"2021","unstructured":"Nguyen, D., Gupta, S., Rana, S., Shilton, A., Venkatesh, S.: Fairness improvement for black-box classifiers with gaussian process. Inf. Sci. 576, 542\u2013556 (2021)","journal-title":"Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Nguyen, D., Luo, W., Nguyen, T., Venkatesh, S., Phung, D.: Learning graph representation via frequent subgraphs. In: SDM, pp. 306\u2013314. SIAM (2018)","key":"3_CR30","DOI":"10.1137\/1.9781611975321.35"},{"key":"3_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/978-3-030-10928-8_34","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"D Nguyen","year":"2019","unstructured":"Nguyen, D., Luo, W., Nguyen, T.D., Venkatesh, S., Phung, D.: Sqn2Vec: learning sequence representation via sequential patterns with a gap constraint. In: Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11052, pp. 569\u2013584. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10928-8_34"},{"key":"3_CR32","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.jbi.2016.03.016","volume":"61","author":"N Nissim","year":"2016","unstructured":"Nissim, N., Boland, M.R., Tatonetti, N.P., Elovici, Y., Hripcsak, G., Shahar, Y., Moskovitch, R.: Improving condition severity classification with an efficient active learning based framework. J. Biomed. Inform. 61, 44\u201354 (2016)","journal-title":"J. Biomed. Inform."},{"key":"3_CR33","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","volume":"97","author":"I Portugal","year":"2018","unstructured":"Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst. Appl. 97, 205\u2013227 (2018)","journal-title":"Expert Syst. Appl."},{"key":"3_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01560-1","volume-title":"Active Learning","author":"B Settles","year":"2012","unstructured":"Settles, B.: Active Learning. Morgan & Claypool Publishers, Cham (2012)"},{"key":"3_CR35","first-page":"1289","volume":"20","author":"B Settles","year":"2007","unstructured":"Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. Adv. Neural. Inf. Process. Syst. 20, 1289\u20131296 (2007)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 1528\u20131540 (2016)","key":"3_CR36","DOI":"10.1145\/2976749.2978392"},{"issue":"1","key":"3_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019)","journal-title":"J. Big Data"},{"doi-asserted-by":"crossref","unstructured":"Walawalkar, D., Shen, Z., Liu, Z., Savvides, M.: Attentive cutmix: an enhanced data augmentation approach for deep learning based image classification. arXiv preprint arXiv:2003.13048 (2020)","key":"3_CR38","DOI":"10.1109\/ICASSP40776.2020.9053994"},{"issue":"1","key":"3_CR39","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1109\/TVCG.2015.2467931","volume":"22","author":"J Wang","year":"2015","unstructured":"Wang, J., Mueller, K.: The visual causality analyst: an interactive interface for causal reasoning. Trans. Vis. Comput. Graphics 22(1), 230\u2013239 (2015)","journal-title":"Trans. Vis. Comput. Graphics"},{"doi-asserted-by":"crossref","unstructured":"Wu, T., Ribeiro, M.T., Heer, J., Weld, D.S.: POLYJUICE: generating counterfactuals for explaining, evaluating, and improving models. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (2021)","key":"3_CR40","DOI":"10.18653\/v1\/2021.acl-long.523"},{"key":"3_CR41","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.patcog.2018.06.004","volume":"83","author":"Y Yang","year":"2018","unstructured":"Yang, Y., Loog, M.: A benchmark and comparison of active learning for logistic regression. Pattern Recogn. 83, 401\u2013415 (2018)","journal-title":"Pattern Recogn."},{"unstructured":"Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P., Weller, A.: From parity to preference-based notions of fairness in classification. arXiv preprint arXiv:1707.00010 (2017)","key":"3_CR42"},{"unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)","key":"3_CR43"},{"doi-asserted-by":"crossref","unstructured":"\u017dliobaite, I., Kamiran, F., Calders, T.: Handling conditional discrimination. In: Proceedings of the IEEE International Conference on Data Mining, pp. 992\u20131001 (2011)","key":"3_CR44","DOI":"10.1109\/ICDM.2011.72"}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21743-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:00:43Z","timestamp":1710262843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21743-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217425","9783031217432"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21743-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}