{"id":"W2287774888","doi":"10.1007/s11227-016-1639-5","title":"Improvised methods for tackling big data stream mining challenges: case study of human activity recognition","year":2016,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"Universidade de Macau","keywords":"Data stream mining; Big data; Computer science; Data stream; Stream processing; Data mining; Data pre-processing; Decision tree; Analytics; Outlier; Data science; Preprocessor; Mode (computer interface); Machine learning; Artificial intelligence; Distributed computing","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":[0.008441932,0.0001813028,0.0004060122,0.0002275521,0.0002742718,0.00006645184,0.002247318,0.00005840488,0.000001029917],"category_scores_gemma":[0.0006756486,0.000107861,0.0000726646,0.0001921038,0.00005758557,0.001107152,0.001188198,0.0001959308,2.700513e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005248161,"about_ca_system_score_gemma":0.00008128564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00022659,"about_ca_topic_score_gemma":0.00007169285,"domain_scores_codex":[0.9976416,0.0007973853,0.0007108636,0.0003170512,0.0002568838,0.0002761689],"domain_scores_gemma":[0.9950504,0.002610779,0.0006712771,0.001257293,0.0003373941,0.00007281462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002200729,0.0002380576,0.00007476322,0.00002671968,0.00007496141,0.0000503101,0.005877977,0.000003224492,0.03382983,0.000009500177,0.00003392636,0.9597587],"study_design_scores_gemma":[0.02284434,0.03650082,0.003179098,0.007892475,0.002166186,0.03239926,0.1518912,0.258725,0.4694555,0.01103862,0.0005188165,0.003388686],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5441995,0.00007624551,0.4552036,0.0001136163,0.0001622125,0.0001836669,0.000009638109,0.00004026671,0.0000112563],"genre_scores_gemma":[0.7085664,0.00002710256,0.2911424,0.000007267207,0.0002387989,0.00000184327,0.000001005239,0.00001409758,0.000001139708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9563701,"threshold_uncertainty_score":0.4398443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2956431881915972,"score_gpt":0.419922318577618,"score_spread":0.1242791303860208,"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."}}