{"id":"W2027846664","doi":"10.1007/s11634-012-0110-6","title":"Time series classification by class-specific Mahalanobis distance measures","year":2012,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Mahalanobis distance; Covariance matrix; Dynamic time warping; Covariance; Pattern recognition (psychology); Series (stratigraphy); k-nearest neighbors algorithm; Margin (machine learning)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000939471,0.0002256162,0.000399092,0.0002959714,0.0002287182,0.0002795138,0.001165141,0.0000881819,0.00004847581],"category_scores_gemma":[0.00007087881,0.0002029135,0.0000812561,0.00228788,0.000175353,0.00638432,0.0002829956,0.0001480238,0.00004874728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007514044,"about_ca_system_score_gemma":0.00001564254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134268,"about_ca_topic_score_gemma":0.0001622804,"domain_scores_codex":[0.9976102,0.0001508901,0.0005901475,0.0008384897,0.0004054001,0.000404862],"domain_scores_gemma":[0.997465,0.0001002508,0.000392254,0.001817665,0.00009151844,0.0001333767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005241734,0.0004546623,0.1644502,0.00004472736,0.0004753761,0.000001826333,0.0008330108,0.0002348966,0.01392618,0.138846,0.007524646,0.6731561],"study_design_scores_gemma":[0.000186654,0.00002203009,0.09687722,0.0000177123,0.000227395,0.000004112514,0.0004736885,0.2674829,0.0002352529,0.0005322387,0.6334884,0.000452416],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004716881,0.0230635,0.9666489,0.001610948,0.0001359104,0.0001758292,0.0001856986,0.0001246244,0.003337721],"genre_scores_gemma":[0.9734232,0.01005992,0.01410349,0.00005366936,0.00009073432,0.00003199872,0.001566519,0.00001364916,0.0006568089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9687063,"threshold_uncertainty_score":0.8274572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03447962751202253,"score_gpt":0.2752292003416775,"score_spread":0.240749572829655,"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."}}