{"id":"W4379983287","doi":"10.1109/icit58465.2023.10143149","title":"A Hybrid Continual Learning Approach for Efficient Hierarchical Classification of IT Support Tickets in the Presence of Class Overlap","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Western University","funders":"","keywords":"Computer science; Class hierarchy; Hierarchy; Task (project management); Inference; Artificial intelligence; Machine learning; Class (philosophy); Ticket; Multiclass classification; Key (lock); Data mining; Support vector machine; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001990692,0.0007565017,0.00120927,0.001498783,0.0006533618,0.001705572,0.002589651,0.00123307,0.001968623],"category_scores_gemma":[0.004312308,0.0004787621,0.0007549252,0.001660836,0.0006465698,0.002930423,0.001523622,0.002087628,0.001367299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009672541,"about_ca_system_score_gemma":0.001518652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006439811,"about_ca_topic_score_gemma":0.009190107,"domain_scores_codex":[0.9989333,0.0002649729,0.00007634968,0.0003560875,0.0002190542,0.0001502364],"domain_scores_gemma":[0.9976915,0.001123689,0.0002118713,0.0003300097,0.0004862742,0.0001566816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008178499,0.00116794,0.01363679,0.0001355368,0.0001342564,0.0002614323,0.0004481702,0.2357936,0.005011206,0.005792744,0.007306806,0.7294936],"study_design_scores_gemma":[0.000009457823,0.00003482258,0.0003387179,0.000003912288,0.0000071998,0.00001831359,0.00003319304,0.996225,0.0004873218,0.002467787,0.0003690714,0.000005234984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0882602,0.0006425945,0.9046136,0.0009422521,0.00008206115,0.0001381713,0.0003987841,0.003016443,0.001905954],"genre_scores_gemma":[0.7948065,0.0002488973,0.1960296,0.0004417534,0.0002235489,0.0002331643,0.00151278,0.0001780072,0.006325817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006439811,"threshold_uncertainty_score":0.01280463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04476575027070322,"score_gpt":0.305961615575104,"score_spread":0.2611958653044008,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}