{"id":"W2073798166","doi":"10.1142/s0218194008003532","title":"SOFTWARE EFFORT ESTIMATION BY ANALOGY USING ATTRIBUTE SELECTION BASED ON ROUGH SET ANALYSIS","year":2008,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Weighting; Data mining; Selection (genetic algorithm); Computer science; Set (abstract data type); Analogy; Estimation; Rough set; Artificial intelligence; Machine learning; Engineering","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":[0.006186648,0.0009701849,0.002047051,0.004449554,0.0005845524,0.001685059,0.001188549,0.0005876936,0.0006406427],"category_scores_gemma":[0.0219352,0.0004816979,0.001707157,0.002944429,0.0004902846,0.002292103,0.001245389,0.0009440653,0.0001563546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008849225,"about_ca_system_score_gemma":0.001195969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002111389,"about_ca_topic_score_gemma":0.001381178,"domain_scores_codex":[0.9938802,0.003218297,0.0004792736,0.0005381372,0.001726602,0.0001574897],"domain_scores_gemma":[0.9863073,0.009878566,0.001130544,0.0009982252,0.001537739,0.0001475801],"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":[0.0002194285,0.0002487583,0.01930665,0.0002880114,0.0004194516,0.0002113633,0.000534953,0.629682,0.003786451,0.0192794,0.001352457,0.3246711],"study_design_scores_gemma":[0.00002439709,0.00009284768,0.00258242,0.00001556868,0.0000521975,0.00006311364,0.00005489462,0.9792859,0.001810559,0.01544353,0.0005391601,0.00003550223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06100012,0.0001418111,0.9375916,0.00009873403,0.00001578363,0.0001168083,0.00007782949,0.0002921403,0.0006652631],"genre_scores_gemma":[0.563766,0.0001548763,0.4351901,0.00002638852,0.00002386347,0.0002734705,0.0002495188,0.00002675581,0.0002890521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006186648,"threshold_uncertainty_score":0.03271854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744243819242585,"score_gpt":0.2725042033759681,"score_spread":0.2550617651835422,"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."}}