{"id":"W2112982660","doi":"10.1109/eorsa.2008.4620334","title":"Investigation of diversity and accuracy in ensemble of classifiers using Bayesian decision rules","year":2008,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Hong Kong Government; University of Hong Kong","keywords":"Artificial intelligence; Classifier (UML); Computer science; Machine learning; Naive Bayes classifier; Random subspace method; Pattern recognition (psychology); Bayesian probability; Majority rule; Test data; Quadratic classifier; Data mining; Support vector machine","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":[0.01230988,0.0008938343,0.001940167,0.00330965,0.0007627441,0.001681535,0.001063858,0.001339706,0.0003849732],"category_scores_gemma":[0.03019513,0.000450791,0.001334006,0.001691387,0.0004867827,0.003098862,0.0009701609,0.0009266032,0.0001980943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008373656,"about_ca_system_score_gemma":0.0005907642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00368252,"about_ca_topic_score_gemma":0.002144047,"domain_scores_codex":[0.993828,0.001872269,0.0005627648,0.001004414,0.002307014,0.0004255696],"domain_scores_gemma":[0.9749702,0.01656706,0.001455514,0.001711015,0.004923184,0.0003730546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001074338,0.0002686526,0.09427279,0.0002404333,0.0009513459,0.0003561527,0.0009269244,0.4744516,0.01394479,0.002984465,0.0009107354,0.4096178],"study_design_scores_gemma":[0.00001127129,0.0002685635,0.01210469,0.00002426932,0.0001427207,0.0001183658,0.0001069404,0.9805492,0.004828107,0.001443146,0.0003673643,0.00003542613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6850449,0.001308624,0.3102478,0.0002384531,0.00007384853,0.00008798019,0.000194671,0.0004040161,0.002399794],"genre_scores_gemma":[0.9548043,0.0002455759,0.04435205,0.00002673776,0.0000455368,0.00003470355,0.000195247,0.00002300044,0.0002729166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01230988,"threshold_uncertainty_score":0.06510162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07469978510964467,"score_gpt":0.2497125470235251,"score_spread":0.1750127619138804,"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."}}