{"id":"W2077649642","doi":"10.4236/ajibm.2012.21002","title":"Comparison of Analytic Hierarchy Process and Dominance-Based Rough Set Approach as Multi-Criteria Decision Aid Methods for the Selection of Investment Projects","year":2012,"lang":"en","type":"article","venue":"American Journal of Industrial and Business Management","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Analytic hierarchy process; Ranking (information retrieval); Rough set; Dominance-based rough set approach; Computer science; Hierarchy; Operations research; Selection (genetic algorithm); Investment (military); Dominance (genetics); Set (abstract data type); Decision rule; Multiple-criteria decision analysis; Decision analysis; Data mining; Management science; Artificial intelligence; Mathematics; Statistics; Economics","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":[0.0162674,0.001149694,0.001632891,0.007647635,0.0006894979,0.002808259,0.001057719,0.0008935763,0.001206899],"category_scores_gemma":[0.03128399,0.0005132569,0.00157646,0.004053231,0.0006851073,0.002258672,0.001501304,0.001102453,0.0003437002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465135,"about_ca_system_score_gemma":0.00257038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002694997,"about_ca_topic_score_gemma":0.003430129,"domain_scores_codex":[0.9750151,0.0157981,0.0007322413,0.0004909528,0.00753809,0.0004255537],"domain_scores_gemma":[0.9814364,0.01341567,0.0009991288,0.0005785691,0.003225375,0.000344774],"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":[0.0008709039,0.0004529273,0.003744449,0.001615001,0.0007713133,0.0002281712,0.001479376,0.2781264,0.004110706,0.06468553,0.003316977,0.6405982],"study_design_scores_gemma":[0.0001671316,0.0006806116,0.002874302,0.0002227797,0.000172938,0.0001593677,0.000565519,0.9413273,0.002565203,0.04518855,0.005937187,0.0001391297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05539224,0.003167439,0.9320779,0.0005397788,0.0001583668,0.0004582872,0.0001548954,0.0003001023,0.007751031],"genre_scores_gemma":[0.4093246,0.001970018,0.5860022,0.0001395832,0.00007432088,0.0007442596,0.0002029888,0.00006160794,0.001480388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0162674,"threshold_uncertainty_score":0.08603126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1188420654867047,"score_gpt":0.3980730194267946,"score_spread":0.2792309539400899,"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."}}