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Methodol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    <jats:bold>Machine Learning (ML)<\/jats:bold>\n                    software employs statistical algorithms to perform high-stake tasks in our daily lives, whose results are usually discriminatory due to protected features (e.g., gender), i.e., one part (called privileged, e.g., male) may be more likely to obtain beneficial decisions than the other part (called unprivileged, e.g., female). In alleviating the unfairness, developers have obtained widely held beliefs about the tradeoff between performance and fairness for ML software. Surprisingly, recent research on feature engineering suggests that enlarging the feature set is the perfect way to kill two birds with one stone, i.e., achieving both higher performance and fairness. However, the experiments used in the prior study did not remove the effect of protected features, which have been suggested to be excluded in both industrial applications and academic studies. As a result, the study did not fully explore the tradeoff between performance and fairness.\n                  <\/jats:p>\n                  <jats:p>\n                    In this article, we first conduct an empirical study to replicate this prior study after excluding the protected features and observe that there is still a tradeoff between performance and fairness with enlarging the features, i.e., more features are not perfect, which would lead to higher performance and lower fairness. Due to more features causing more collection and pre-processing budgets, we aim to search for an effective alternative. Inspired by the \u201cless is more\u201d principle, we propose a novel feature ranking method,\n                    <jats:bold>Hybrid-importance and Early-validation based Feature Ranking (HEFR)<\/jats:bold>\n                    , to find an efficient subset to replace the full feature set with comparable performance and fairness. Our method, HEFR, employs hybrid feature importances to combine performance and fairness and conducts early validation to check the effectiveness of hybrid importances. We conduct experiments on seven datasets and three classifiers to evaluate our method with five baselines. The results have shown that (a) HEFR is efficient for ML software feature engineering: applying HEFR to choose about 10% of features would construct ML software with better or comparable performance and fairness, and (b) HEFR is actionable with small dataset sizes: applying HEFR with only 10% data size would still help choose the proper feature subset.\n                  <\/jats:p>","DOI":"10.1145\/3730577","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T10:41:21Z","timestamp":1744972881000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Less Is More: Feature Engineering for Fairness and Performance of Machine Learning Software"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2710-3535","authenticated-orcid":false,"given":"Linghan","family":"Meng","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-7175","authenticated-orcid":false,"given":"Yanhui","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2352-2226","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4800-6255","authenticated-orcid":false,"given":"Mingliang","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4645-2526","authenticated-orcid":false,"given":"Yuming","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7743-1296","authenticated-orcid":false,"given":"Baowen","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Statlog (German Credit Data) Data Set. 1994. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/statlog+(german+ credit+data)"},{"key":"e_1_3_3_3_2","unstructured":"Adult Data Set. 1996. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/adult"},{"key":"e_1_3_3_4_2","unstructured":"Bank Marketing Data Set. 2012. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Bank+Marketing"},{"key":"e_1_3_3_5_2","unstructured":"MEPS Data Set. 2015. Retrieved from https:\/\/meps.ahrq.gov\/mepsweb"},{"key":"e_1_3_3_6_2","unstructured":"Amazon Just Showed Us That Unbiased Algorithms Can Be Inadvertently Racist. 2016. Retrieved from https:\/\/www.businessinsider.com\/how-algorithms-can-be-racist-2016-4"},{"key":"e_1_3_3_7_2","unstructured":"Default of Credit Card Clients Data Set. 2016. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/default+of+credit+ card+clients"},{"key":"e_1_3_3_8_2","unstructured":"Compas-Analysis. 2017. Retrieved from https:\/\/github.com\/propublica\/compas-analysis"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338937"},{"key":"e_1_3_3_10_2","first-page":"291","volume-title":"Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP \u201919)","author":"Amershi S.","year":"2019","unstructured":"S. Amershi, A. Begel, C. Bird, R. DeLine, H. C. Gall, E. Kamar, N. Nagappan, B. Nushi, and T. Zimmermann. 2019. Software engineering for machine learning: A case study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP \u201919). H. Sharp and M. Whalen (Eds.), IEEE\/ACM, 291\u2013300."},{"key":"e_1_3_3_11_2","unstructured":"Anna Montoya Inversion Kirill Odintsov and Martin Kotek. 2018. Home Credit Default Risk. Kaggle. Retrieved from https:\/\/kaggle.com\/competitions\/home-credit-default-risk"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2942287"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013699998"},{"issue":"4175","key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1126\/science.187.4175.398","article-title":"Sex bias in graduate admissions: Data from Berkeley: Measuring bias is harder than is usually assumed, and the evidence is sometimes contrary to expectation","volume":"187","author":"Bickel P. J.","year":"1975","unstructured":"P. J. Bickel, E. A. Hammel, and J. W. O\u2019Connell. 1975. Sex bias in graduate admissions: Data from Berkeley: Measuring bias is harder than is usually assumed, and the evidence is sometimes contrary to expectation. 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Aeberhard, G. \u00c7alikli, and A. Bacchelli. 2022. Less is more: Supporting developers in vulnerability detection during code review. In Proceedings of the 44th International Conference on Software Engineering (ICSE \u201922). ACM, New York, NY, 1317\u20131329."},{"key":"e_1_3_3_19_2","first-page":"754","volume-title":"Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/SIGSOFT FSE \u201918)","author":"Brun Y.","year":"2018","unstructured":"Y. Brun and A. Meliou. 2018. Software fairness. In Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/SIGSOFT FSE \u201918). G. T. Leavens, A. Garcia, and C. S. Pasareanu (Eds.), ACM, 754\u2013759."},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0190-x"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468537"},{"key":"e_1_3_3_22_2","first-page":"654","volume-title":"Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE \u201920), Virtual Event","author":"Chakraborty J.","year":"2020","unstructured":"J. Chakraborty, S. Majumder, Z. Yu, and T. Menzies. 2020. Fairway: A way to build fair ML software. In Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE \u201920), Virtual Event. P. Devanbu, M. B. Cohen, and T. Zimmermann (Eds.), ACM, 654\u2013665."},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3418932"},{"key":"e_1_3_3_24_2","unstructured":"J. Chakraborty T. Xia F. M. 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ACM, 921\u2013933."},{"key":"e_1_3_3_35_2","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1145\/3180155.3180217","volume-title":"Proceedings of the 40th International Conference on Software Engineering (ICSE \u201918)","author":"Germ\u00e1n D. M.","year":"2018","unstructured":"D. M. Germ\u00e1n, G. Robles, G. Poo-Caama\u00f1o, X. Yang, H. Iida, and K. Inoue. 2018. \u201cWas my contribution fairly reviewed?\u201d: A framework to study the perception of fairness in modern code reviews. In Proceedings of the 40th International Conference on Software Engineering (ICSE \u201918). M. Chaudron, I. Crnkovic, M. Chechik, and M. Harman (Eds.), ACM, 523\u2013534."},{"key":"e_1_3_3_36_2","first-page":"266","volume-title":"Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI \u201911)","author":"Gu Q.","year":"2011","unstructured":"Q. Gu, Z. Li, and J. Han. 2011. Generalized fisher score for feature selection. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI \u201911). F. G. Cozman and A. Pfeffer (Eds.), AUAI Press, 266\u2013273."},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012487302797"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372831"},{"key":"e_1_3_3_39_2","first-page":"1322","volume-title":"Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE \u201921)","author":"Hort M.","year":"2021","unstructured":"M. Hort and F. Sarro. 2021. Did you do your homework? Raising awareness on software fairness and discrimination. In Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE \u201921). IEEE, 1322\u20131326."},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468565"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.06.004"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05874-8"},{"key":"e_1_3_3_43_2","first-page":"171","volume-title":"Proceedings of the European Conference on (ECML \u201994)","author":"Kononenko I.","year":"1994","unstructured":"I. Kononenko. 1994. Estimating attributes: Analysis and extensions of RELIEF. In Proceedings of the European Conference on (ECML \u201994). F. Bergadano and L. D. Raedt (Eds.), Lecture Notes in Computer Science, Vol. 784, Springer, 171\u2013182."},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510091"},{"key":"e_1_3_3_45_2","first-page":"388","volume-title":"Proceedings of the 7th International Conference on Tools with Artificial Intelligence (ICTAI \u201995)","author":"Liu H.","year":"1995","unstructured":"H. Liu and R. Setiono. 1995. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of the 7th International Conference on Tools with Artificial Intelligence (ICTAI \u201995). IEEE Computer Society, 388\u2013391."},{"key":"e_1_3_3_46_2","first-page":"232","volume-title":"Proceedings of the 25th International Conference on Software Analysis, Evolution and Reengineering (SANER \u201918)","author":"Liu Y.","year":"2018","unstructured":"Y. Liu, Y. Li, J. Guo, Y. Zhou, and B. Xu. 2018. Connecting software metrics across versions to predict defects. In Proceedings of the 25th International Conference on Software Analysis, Evolution and Reengineering (SANER \u201918). R. Oliveto, M. D. Penta, and D. C. Shepherd (Eds.), IEEE Computer Society, 232\u2013243."},{"key":"e_1_3_3_47_2","first-page":"666","volume-title":"Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE \u201920), Virtual Event","author":"Liu Y.","year":"2020","unstructured":"Y. Liu, Y. Li, S. Lin, and R. Zhao. 2020. Towards automated verification of smart contract fairness. In Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE \u201920), Virtual Event. P. Devanbu, M. B. Cohen, and T. Zimmermann (Eds.), ACM, 666\u2013677."},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00045"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2019.2954841"},{"key":"e_1_3_3_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.03.012"},{"issue":"6","key":"e_1_3_3_51_2","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.1109\/TSE.2021.3051492","article-title":"Within-project defect prediction of infrastructure-as-code using product and process metrics","volume":"48","author":"Palma S. D.","year":"2022","unstructured":"S. D. Palma, D. D. Nucci, F. Palomba, and D. A. Tamburri. 2022. Within-project defect prediction of infrastructure-as-code using product and process metrics. 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Citeseer, 1\u201351."},{"key":"e_1_3_3_56_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-662-45620-0_3","volume-title":"Feature Selection for Data and Pattern Recognition","author":"Sta\u0144czyk U.","year":"2015","unstructured":"U. Sta\u0144czyk. 2015. Feature evaluation by filter, wrapper, and embedded approaches. In Feature Selection for Data and Pattern Recognition. Urszula Stanczyk, and Lakhmi C. Jain (Eds.), Springer, 29\u201344."},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549169"},{"key":"e_1_3_3_58_2","first-page":"1122","volume-title":"Proceedings of the 42nd International Conference on Software Engineering (ICSE \u201920)","author":"Tian Y.","year":"2020","unstructured":"Y. Tian, Z. Zhong, V. Ordonez, G. E. Kaiser, and B. Ray. 2020. Testing DNN image classifiers for confusion & bias errors. In Proceedings of the 42nd International Conference on Software Engineering (ICSE \u201920). G. Rothermel and D. Bae (Eds.), ACM, 1122\u20131134."},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510202"},{"key":"e_1_3_3_60_2","first-page":"743","volume-title":"Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE \u201921)","author":"Uddin M. K.","year":"2021","unstructured":"M. K. Uddin, Q. He, J. Han, and C. Chua. 2021. Mining cross-domain apps for software evolution: A feature-based approach. In Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE \u201921). IEEE, 743\u2013755."},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238165"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487571"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2877612"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00088"},{"key":"e_1_3_3_65_2","first-page":"10414","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), the 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), the 11th Symposium on Educational Advances in Artificial Intelligence (EAAI \u201921)","author":"Xiang Z.","year":"2021","unstructured":"Z. Xiang, M. Fan, G. V. Tovar, W. Trehern, B. Yoon, X. Qian, R. Arr\u00f3yave, and X. Qian. 2021. Physics-constrained automatic feature engineering for predictive modeling in materials science. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI \u201921), the 33rd Conference on Innovative Applications of Artificial Intelligence (IAAI \u201921), the 11th Symposium on Educational Advances in Artificial Intelligence (EAAI \u201921), Virtual Event. AAAI Press, 10414\u201310421."},{"key":"e_1_3_3_66_2","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1145\/3180155.3180257","volume-title":"Proceedings of the 40th International Conference on Software Engineering (ICSE \u201918)","author":"Xue Y.","year":"2018","unstructured":"Y. Xue and Y. Li. 2018. Multi-objective integer programming approaches for solving optimal feature selection problem: A new perspective on multi-objective optimization problems in SBSE. In Proceedings of the 40th International Conference on Software Engineering (ICSE \u201918). M. Chaudron, I. Crnkovic, M. Chechik, and M. Harman (Eds.), ACM, 1231\u20131242."},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678630"},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00129"},{"key":"e_1_3_3_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2962027"},{"key":"e_1_3_3_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464820"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549103"},{"key":"e_1_3_3_72_2","first-page":"949","volume-title":"Proceedings of the 42nd International Conference on Software Engineering (ICSE \u201920)","author":"Zhang P.","year":"2020","unstructured":"P. Zhang, J. Wang, J. Sun, G. Dong, X. Wang, X. Wang, J. S. Dong, and T. Dai. 2020. White-box fairness testing through adversarial sampling. In Proceedings of the 42nd International Conference on Software Engineering (ICSE \u201920). G. Rothermel and D. 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