{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90800"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90800","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DPLearn: an effective but concise learning framework based on discriminative patterns","abstract":"Pattern-based classification was originally proposed to improve the accuracy using selected frequent patterns, where many efforts were paid to prune a huge number of non-discriminative frequent patterns. On the other hand, tree-based models have shown strong abilities on many learning tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, a natural and effective way is proposed to resolve pattern-based learning tasks by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.g., random forest). Moreover, the number of discriminative patterns is further compressed by selecting the most effective pattern combinations that fit into a generalized linear model. Note that this method is a general framework, which is applicable on both classification and regression tasks by using different loss functions. Extensive experiments demonstrate that the discriminative pattern-based learning framework (DPLearn) could perform as good as previous state-of-the-art algorithms, provide great interpretability by utilizing only very limited number of discriminative patterns, and predict new data extremely fast. More specifically, in classification tasks, DPLearn could gain even better accuracy by only using top-20 discriminative patterns, while in regression tasks DPLearn delivers reasonable performance that is comparable to complex models, which shows that framework so generated is very concise and highly explanatory to human experts.","abstract_html":"Pattern-based classification was originally proposed to improve the accuracy using selected frequent patterns, where many efforts were paid to prune a huge number of non-discriminative frequent patterns. On the other hand, tree-based models have shown strong abilities on many learning tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, a natural and effective way is proposed to resolve pattern-based learning tasks by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.g., random forest). Moreover, the number of discriminative patterns is further compressed by selecting the most effective pattern combinations that fit into a generalized linear model. Note that this method is a general framework, which is applicable on both classification and regression tasks by using different loss functions. Extensive experiments demonstrate that the discriminative pattern-based learning framework (DPLearn) could perform as good as previous state-of-the-art algorithms, provide great interpretability by utilizing only very limited number of discriminative patterns, and predict new data extremely fast. More specifically, in classification tasks, DPLearn could gain even better accuracy by only using top-20 discriminative patterns, while in regression tasks DPLearn delivers reasonable performance that is comparable to complex models, which shows that framework so generated is very concise and highly explanatory to human experts.","abstract_has_math":false,"creators":["Tong, Wenzhu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:27:48Z","date_published":"2016-07-07T20:27:48Z","updated_at":"2026-07-22T22:26:34Z","subjects":["DPLearn","discriminative patterns"],"languages":["en"],"rights":["Copyright 2016 Wenzhu Tong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90800","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Tong, Wenzhu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:27:48Z","2018-07-08T09:15:33Z","2016-04-20","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer 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":["DPLearn","discriminative patterns"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Wenzhu Tong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90800"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Pattern-based classification was originally proposed to improve the accuracy using selected frequent patterns, where many efforts were paid to prune a huge number of non-discriminative frequent patterns. On the other hand, tree-based models have shown strong abilities on many learning tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, a natural and effective way is proposed to resolve pattern-based learning tasks by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.g., random forest). Moreover, the number of discriminative patterns is further compressed by selecting the most effective pattern combinations that fit into a generalized linear model. Note that this method is a general framework, which is applicable on both classification and regression tasks by using different loss functions. Extensive experiments demonstrate that the discriminative pattern-based learning framework (DPLearn) could perform as good as previous state-of-the-art algorithms, provide great interpretability by utilizing only very limited number of discriminative patterns, and predict new data extremely fast. More specifically, in classification tasks, DPLearn could gain even better accuracy by only using top-20 discriminative patterns, while in regression tasks DPLearn delivers reasonable performance that is comparable to complex models, which shows that framework so generated is very concise and highly explanatory to human experts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Wenzhu Tong, accepted the attached license on 2016-04-19 at 21:07.","The student, Wenzhu Tong, submitted this Thesis for approval on 2016-04-19 at 21:36.","This Thesis was approved for publication on 2016-04-20 at 10:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9358 on 2016-07-07 at 13:50:15","Made available in DSpace on 2016-07-07T20:27:48Z (GMT). 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On the other hand, tree-based models have shown strong abilities on many learning tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, a natural and effective way is proposed to resolve pattern-based learning tasks by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.g., random forest). Moreover, the number of discriminative patterns is further compressed by selecting the most effective pattern combinations that fit into a generalized linear model. Note that this method is a general framework, which is applicable on both classification and regression tasks by using different loss functions. Extensive experiments demonstrate that the discriminative pattern-based learning framework (DPLearn) could perform as good as previous state-of-the-art algorithms, provide great interpretability by utilizing only very limited number of discriminative patterns, and predict new data extremely fast. More specifically, in classification tasks, DPLearn could gain even better accuracy by only using top-20 discriminative patterns, while in regression tasks DPLearn delivers reasonable performance that is comparable to complex models, which shows that framework so generated is very concise and highly explanatory to human experts.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Wenzhu Tong, accepted the attached license on 2016-04-19 at 21:07.","The student, Wenzhu Tong, submitted this Thesis for approval on 2016-04-19 at 21:36.","This Thesis was approved for publication on 2016-04-20 at 10:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9358 on 2016-07-07 at 13:50:15","Made available in DSpace on 2016-07-07T20:27:48Z (GMT). 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