{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:24:32Z","timestamp":1742948672476,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031226946"},{"type":"electronic","value":"9783031226953"}],"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\/978-3-031-22695-3_44","type":"book-chapter","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T15:11:58Z","timestamp":1669993918000},"page":"631-645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing the\u00a0Feature Set for\u00a0Machine Learning Charitable Predictions"],"prefix":"10.1007","author":[{"given":"Greg","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jordan","family":"Pippy","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Hobbs","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"issue":"6","key":"44_CR1","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1086\/261662","volume":"97","author":"J Andreoni","year":"1989","unstructured":"Andreoni, J.: Giving with impure altruism: applications to charity and Ricardian equivalence. J. Polit. Econ. 97(6), 447\u201358 (1989)","journal-title":"J. Polit. Econ."},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Apte, C., Bibelnieks, E., Natajaran, R., Pednault, E., Tipu, F., Campbell, D.: Segmentation-based modeling for advanced targeted marketing. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 408\u2013413 (2001)","DOI":"10.1145\/502512.502573"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Bekkers, R., Wiepking, P.: A literature review of empirical studies of philanthropy: eight mechanisms that drive charitable giving. Nonprofit Voluntary Sector Q. 40(5), 924\u2013973 (2011). http:\/\/journals.sagepub.com\/doi\/10.1177\/0899764010380927","DOI":"10.1177\/0899764010380927"},{"key":"44_CR4","unstructured":"Benefactor: Giving USA 2022. benefactorgroup.com\/givingusa2022\/. June 2022"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.eswa.2005.11.037","volume":"32","author":"J Burez","year":"2005","unstructured":"Burez, J., Van den Poel, D.: CRM at a pay-TV company: using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst. Appl. 32, 277\u2013288 (2005)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"44_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00327-4","volume":"7","author":"R-C Chen","year":"2020","unstructured":"Chen, R.-C., Dewi, C., Huang, S.-W., Caraka, R.E.: Selecting critical features for data classification based on machine learning methods. J. Big Data 7(1), 1\u201326 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00327-4","journal-title":"J. Big Data"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on EMNLP, pp. 423?430. EMNLP 06, Association for Computational Linguistics, USA (2006)","DOI":"10.3115\/1610075.1610135"},{"key":"44_CR8","unstructured":"Lee, G., Adunoor, S., Hobbs, M.: Machine learning across charities. In: Proceedings of the 17th Modeling Decision in Artificial Intelligence Conference (2020). in press"},{"key":"44_CR9","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-981-16-3357-7_12","volume-title":"Deep Learning Applications, Volume 3","author":"G Lee","year":"2022","unstructured":"Lee, G., Raghavan, A.K., Hobbs, M.: Deep learning the donor journey with convolutional and recurrent neural networks. In: Wani, M.A., Raj, B., Luo, F., Dou, D. (eds.) Deep Learning Applications, Volume 3. AISC, vol. 1395, pp. 295\u2013320. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-3357-7_12"},{"key":"44_CR10","doi-asserted-by":"publisher","unstructured":"Lee, G., Raghavan, A.K.V., Hobbs, M.: Improving the donor journey with convolutional and recurrent neural networks. In: Wani, M.A., Luo, F., Li, X.A., Dou, D., Bonchi, F. (eds.) 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, Miami, FL, USA, 14\u201317 December 2020, pp. 913\u2013920. IEEE (2020). https:\/\/doi.org\/10.1109\/ICMLA51294.2020.00149","DOI":"10.1109\/ICMLA51294.2020.00149"},{"issue":"1","key":"44_CR11","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1186\/1471-2105-10-213","volume":"10","author":"BH Menze","year":"2009","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., Hamprecht, F.A.: A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10(1), 213 (2009). https:\/\/doi.org\/10.1186\/1471-2105-10-213","journal-title":"BMC Bioinformatics"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Patras, L., Mart\u00ednez-Tur, V., Gracia, E., Moliner, C.: Why do people spend money to help vulnerable people? PLoS ONE 14(3), e0213582 (2019)","DOI":"10.1371\/journal.pone.0213582"},{"key":"44_CR13","unstructured":"Rau, N.: Predictive Modeling of Alumni Donors: an engagement model for fundraising in postsecondary education. Ph.D. thesis, James Madison (2014)"},{"key":"44_CR14","unstructured":"Shockley, C.C.: The Relationship Between Student Engagement and Alumni Giving at Higher Education Institutions: A comparative case study analysis. Ph.D. thesis, Department of Education, Delaware State University (2019)"},{"key":"44_CR15","unstructured":"Ye, L.: A Machine Learning Approach to Fundraising Success in Higher Education. Master\u2019s thesis, University of Victoria (2017)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Yuan, R., Xue, D., Xu, Y., Xue, D., Li, J.: Machine learning combined with feature engineering to search for BaTiO3 based ceramics with large piezoelectric constant. J. Alloys Compounds 908, 164468 (2022). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925838822008593","DOI":"10.1016\/j.jallcom.2022.164468"}],"container-title":["Lecture Notes in Computer Science","AI 2022: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22695-3_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:30:43Z","timestamp":1710257443000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22695-3_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031226946","9783031226953"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22695-3_44","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":"3 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"5 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"90","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}