{"id":"W2955844827","doi":"10.1016/j.pmcj.2019.101045","title":"An incremental learning method based on formal concept analysis for pattern recognition in nonstationary sensor-based smart environments","year":2019,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Retraining; Streaming data; Concept drift; Adaptation (eye); Machine learning; Incremental learning; Artificial intelligence; Activity recognition; Data mining; Data stream mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00128598,0.000504354,0.0007752709,0.001415904,0.0004458867,0.001000525,0.002079714,0.0005036888,0.003072675],"category_scores_gemma":[0.005336152,0.0003652258,0.001173449,0.0009935166,0.0006806932,0.001978101,0.001290404,0.001225877,0.0005606747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005945343,"about_ca_system_score_gemma":0.00128323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003378673,"about_ca_topic_score_gemma":0.003892753,"domain_scores_codex":[0.9990296,0.0001914164,0.00008469472,0.0002338432,0.000390446,0.00007005547],"domain_scores_gemma":[0.9977511,0.001290731,0.0001252064,0.0002300655,0.0005403588,0.00006252596],"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":[0.0001937552,0.0002557086,0.002000019,0.000314167,0.0001482971,0.0002269181,0.0003489361,0.08789656,0.01283085,0.07482263,0.003883166,0.817079],"study_design_scores_gemma":[0.00002356335,0.0000527437,0.0002699487,0.00001563581,0.00003847362,0.00009500197,0.00002953714,0.9692638,0.003313875,0.02441892,0.002462193,0.00001622817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00209232,0.00004002103,0.9970489,0.00002995442,0.00002048202,0.00002893591,0.00002533923,0.0004774373,0.0002366971],"genre_scores_gemma":[0.1116801,0.000106233,0.8863686,0.00007852138,0.00003424752,0.0001801372,0.0002188774,0.0001067966,0.001226493],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003378673,"threshold_uncertainty_score":0.01027912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939411175246113,"score_gpt":0.2921229478228359,"score_spread":0.2727288360703748,"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."}}