{"id":6039,"date":"2023-10-12T12:11:47","date_gmt":"2023-10-12T05:11:47","guid":{"rendered":"https:\/\/www.ict.mahidol.ac.th\/en\/?post_type=tribe_events&p=6039"},"modified":"2023-10-17T22:32:15","modified_gmt":"2023-10-17T15:32:15","slug":"interpretable-decision-tree-ensemble-learning","status":"publish","type":"tribe_events","link":"https:\/\/www.ict.mahidol.ac.th\/en\/event\/interpretable-decision-tree-ensemble-learning\/","title":{"rendered":"\u0e2a\u0e31\u0e21\u0e21\u0e19\u0e32\u0e27\u0e34\u0e0a\u0e32\u0e01\u0e32\u0e23 \u0e40\u0e23\u0e37\u0e48\u0e2d\u0e07 \u201cInterpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification\u201d"},"content":{"rendered":"

[vc_row][vc_column][vc_tta_tabs shape=”square” color=”vista-blue” active_section=”1″][vc_tta_section title=”\u0e2b\u0e25\u0e31\u0e01\u0e01\u0e32\u0e23\u0e41\u0e25\u0e30\u0e40\u0e2b\u0e15\u0e38\u0e1c\u0e25” tab_id=”1697075721949-32301929-150e”][vc_column_text]Abstract:<\/strong><\/p>\n

We marry two powerful ideas: decision tree ensemble for rule induction and abstract argumentation for aggregating inferences from diverse decision trees to produce better predictive performance and intrinsically interpretable than state-of-the-art ensemble models. Our approach called Arguing Tree Ensemble is a self-explainable model that first learns a group of decision trees from a given dataset. It then treats all decision trees as knowledgeable agents and lets them argue with each other to conclude a prediction. Unlike conventional ensemble methods, this proposal offers full transparency to the prediction process. Therefore, AI users are able to interpret and diagnose the prediction\u2019s output.<\/p>\n

[\/vc_column_text][vc_single_image image=”6041″ img_size=”full” alignment=”center” onclick=”link_image”][\/vc_tta_section][vc_tta_section title=”\u0e01\u0e33\u0e2b\u0e19\u0e14\u0e01\u0e32\u0e23” tab_id=”1697075721952-c3a7b55b-b8b3″][vc_column_text]<\/p>\n

\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e1a\u0e23\u0e34\u0e01\u0e32\u0e23\u0e27\u0e34\u0e0a\u0e32\u0e01\u0e32\u0e23\u0e2a\u0e39\u0e48\u0e2a\u0e31\u0e07\u0e04\u0e21 \u0e2a\u0e31\u0e21\u0e21\u0e19\u0e32\u0e27\u0e34\u0e0a\u0e32\u0e01\u0e32\u0e23
\n\u0e40\u0e23\u0e37\u0e48\u0e2d\u0e07 Interpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification<\/strong>
\n\u0e42\u0e14\u0e22 Dr. Teeradaj Racharak (Senior Lecturer) Japan Advanced Institute of Science and Technology (JAIST)
\n\u0e27\u0e31\u0e19\u0e1e\u0e38\u0e18\u0e17\u0e35\u0e48 25 \u0e15\u0e38\u0e25\u0e32\u0e04\u0e21 2566 \u0e40\u0e27\u0e25\u0e32 14.00 – 16.00 \u0e19.
\n\u0e13 \u0e2b\u0e49\u0e2d\u0e07 IT210 \u0e0a\u0e31\u0e49\u0e19 2 \u0e04\u0e13\u0e30\u0e40\u0e17\u0e04\u0e42\u0e19\u0e42\u0e25\u0e22\u0e35\u0e2a\u0e32\u0e23\u0e2a\u0e19\u0e40\u0e17\u0e28\u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e2a\u0e37\u0e48\u0e2d\u0e2a\u0e32\u0e23 \u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22\u0e21\u0e2b\u0e34\u0e14\u0e25 \u0e27\u0e34\u0e17\u0e22\u0e32\u0e40\u0e02\u0e15\u0e28\u0e32\u0e25\u0e32\u0e22\u0e32<\/p>\n\n\n\n\n\n\n\n\n
\u0e40\u0e27\u0e25\u0e32<\/strong><\/td>\n\u0e2b\u0e31\u0e27\u0e02\u0e49\u0e2d\u0e41\u0e25\u0e30\u0e01\u0e34\u0e08\u0e01\u0e23\u0e23\u0e21<\/strong><\/td>\n<\/tr>\n<\/thead>\n
13.00 \u2013 14.00 \u0e19.<\/td>\n\u0e25\u0e07\u0e17\u0e30\u0e40\u0e1a\u0e35\u0e22\u0e19<\/td>\n<\/tr>\n
14.00 \u2013 14.10 \u0e19.<\/td>\n\u0e01\u0e25\u0e48\u0e32\u0e27\u0e15\u0e49\u0e2d\u0e19\u0e23\u0e31\u0e1a\u0e41\u0e25\u0e30\u0e1e\u0e34\u0e18\u0e35\u0e40\u0e1b\u0e34\u0e14\u0e01\u0e32\u0e23\u0e2a\u0e31\u0e21\u0e21\u0e19\u0e32<\/td>\n<\/tr>\n
14.10 \u2013 15.50 \u0e19.<\/td>\n\u0e2a\u0e31\u0e21\u0e21\u0e19\u0e32\u0e40\u0e23\u0e37\u0e48\u0e2d\u0e07 \u201cInterpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification\u201d<\/td>\n<\/tr>\n
15.50 \u2013 16.00 \u0e19.<\/td>\nQ & A \u0e01\u0e25\u0e48\u0e32\u0e27\u0e1b\u0e34\u0e14\u0e41\u0e25\u0e30\u0e16\u0e48\u0e32\u0e22\u0e23\u0e39\u0e1b\u0e23\u0e48\u0e27\u0e21\u0e01\u0e31\u0e19<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”\u0e27\u0e34\u0e17\u0e22\u0e32\u0e01\u0e23” tab_id=”1697075757186-9c691b3d-e5c9″][vc_empty_space][vc_single_image image=”6042″ alignment=”center”][vc_column_text]Dr. Teeradaj Racharak (Senior Lecturer)<\/b>
\nJapan Advanced Institute of Science and Technology (JAIST)<\/p>\n

Bio:<\/strong><\/p>\n

Teeradaj Racharak is a Senior Lecturer (Junior Associate Professor) at the School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), and runs ReaLearn (the Reasoning & Learning for Trustworthy AI laboratory) at JAIST.\u00a0 Before JAIST, he was a software and DevOps engineer, involved in the development of large-scale web applications including MangaMagazine.net and Inkblazers. He is broadly interested in mathematical modeling and implementation of AI. He specializes in logic and machine learning, particularly in Description Logic, Computational Argumentation, and Deep Learning. He has a diverse educational background and work experience in universities and software industries. His research interests (but are not limited to) span across: Knowledge Representation and Reasoning (KRR) for Explainable AI, Machine Learning (ML) and its applications, and Integration of KRR and ML for Robust and Explainable AI.<\/p>\n

[\/vc_column_text][\/vc_tta_section][vc_tta_section title=”\u0e25\u0e07\u0e17\u0e30\u0e40\u0e1a\u0e35\u0e22\u0e19” tab_id=”1697075775516-a5d0d447-4cce”][vc_btn title=”\u0e25\u0e07\u0e17\u0e30\u0e40\u0e1a\u0e35\u0e22\u0e19 \u0e04\u0e25\u0e34\u0e01” color=”warning” link=”url:https%3A%2F%2Fwww2.ict.mahidol.ac.th%2Facademicservices%2FTechTransferRegis%2FRegister.aspx%3FEventID%3D399|title:Click|target:_blank”][\/vc_tta_section][\/vc_tta_tabs][\/vc_column][\/vc_row]<\/p>\n<\/section>","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][vc_tta_tabs shape=”square” color=”vista-blue” active_section=”1″][vc_tta_section title=”\u0e2b\u0e25\u0e31\u0e01\u0e01\u0e32\u0e23\u0e41\u0e25\u0e30\u0e40\u0e2b\u0e15\u0e38\u0e1c\u0e25” tab_id=”1697075721949-32301929-150e”][vc_column_text]Abstract: We marry two powerful ideas: decision tree ensemble for rule induction and abstract argumentation for aggregating inferences from diverse decision trees to produce better predictive performance and intrinsically interpretable than state-of-the-art ensemble models. Our approach called Arguing Tree Ensemble is a self-explainable model that first learns a group of decision trees from a given […]<\/p>\n","protected":false},"author":1,"featured_media":6040,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":[],"_tribe_ticket_has_attendee_info_fields":false,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[55],"class_list":["post-6039","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-seminar","cat_seminar"],"ticketed":false,"_links":{"self":[{"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tribe_events\/6039","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/comments?post=6039"}],"version-history":[{"count":2,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tribe_events\/6039\/revisions"}],"predecessor-version":[{"id":6308,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tribe_events\/6039\/revisions\/6308"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/media\/6040"}],"wp:attachment":[{"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/media?parent=6039"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tags?post=6039"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.ict.mahidol.ac.th\/en\/wp-json\/wp\/v2\/tribe_events_cat?post=6039"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}