{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110877"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110877","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Imbalanced learning using actuarial modified loss function in tree-based models","abstract":"The point mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel actuarial modified loss functions, namely, weighted sum of squared error and Canberra loss functions. This modification imposes a significant penalty on grouping nonzero observations with zero ones at the splitting procedure. We examine and compare the predictive performance of such actuarial modified tree-based models in relation to the traditional models in a synthetic dataset. Our studies show that, such modification results in improved prediction and completely different tree structures.","abstract_html":"The point mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel actuarial modified loss functions, namely, weighted sum of squared error and Canberra loss functions. This modification imposes a significant penalty on grouping nonzero observations with zero ones at the splitting procedure. We examine and compare the predictive performance of such actuarial modified tree-based models in relation to the traditional models in a synthetic dataset. Our studies show that, such modification results in improved prediction and completely different tree structures.","abstract_has_math":false,"creators":["Hu, Changyue"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Actuarial Science","degree_department":null,"school":null,"contributors":["Quan, Zhiyu","Chong, Alfred"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T04:06:57Z","date_published":"2021-09-17T04:06:57Z","updated_at":"2026-07-22T22:24:52Z","subjects":["tree models, imbalanced learning, predictive modeling"],"languages":["en"],"rights":["Copyright 2021 Changyue Hu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110877","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Quan, Zhiyu","Chong, Alfred"]},{"key":"dc:creator","label":"Author","values":["Hu, Changyue"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T04:06:57Z","2023-09-17T04:07:01Z","2021-04-30","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Actuarial Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["tree models, imbalanced learning, predictive modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Changyue Hu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110877"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The point mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel actuarial modified loss functions, namely, weighted sum of squared error and Canberra loss functions. This modification imposes a significant penalty on grouping nonzero observations with zero ones at the splitting procedure. We examine and compare the predictive performance of such actuarial modified tree-based models in relation to the traditional models in a synthetic dataset. Our studies show that, such modification results in improved prediction and completely different tree structures.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-05-01","The student, Changyue Hu, accepted the attached license on 2021-04-30 at 16:30.","The student, Changyue Hu, submitted this Thesis for approval on 2021-04-30 at 16:36.","This Thesis was approved for publication on 2021-04-30 at 17:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16634 on 2021-09-16 at 20:14:47","Made available in DSpace on 2021-09-17T04:06:57Z (GMT). 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Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis then modifies the function to remedy the imbalance issue presented in the insurance loss modeling. We propose two novel actuarial modified loss functions, namely, weighted sum of squared error and Canberra loss functions. This modification imposes a significant penalty on grouping nonzero observations with zero ones at the splitting procedure. We examine and compare the predictive performance of such actuarial modified tree-based models in relation to the traditional models in a synthetic dataset. Our studies show that, such modification results in improved prediction and completely different tree structures.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-05-01","The student, Changyue Hu, accepted the attached license on 2021-04-30 at 16:30.","The student, Changyue Hu, submitted this Thesis for approval on 2021-04-30 at 16:36.","This Thesis was approved for publication on 2021-04-30 at 17:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16634 on 2021-09-16 at 20:14:47","Made available in DSpace on 2021-09-17T04:06:57Z (GMT). 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