matlab中存档算法代码-Credit-default-prediction-MLP-SVM:这是一项使用两种机器学习模型（多层感知器和支持

[{"title":"（ 58 个子文件 74.41MB ） matlab中存档算法代码-Credit-default-prediction-MLP-SVM:这是一项使用两种机器学习模型（多层感知器和支持","children":[{"title":"Credit-default-prediction-MLP-SVM-master","children":[{"title":"LICENSE <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"Comparative study on Multilayer Perceptron and Support Vector Machine","children":[{"title":"Matlab code","children":[{"title":"All MLP models","children":[{"title":"Training in original dataset","children":[{"title":"MLP all features","children":[{"title":"MLP_baseline.m <span style='color:#111;'> 2.99KB </span>","children":null,"spread":false},{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_3hidden_25nodes_allfeats_original.asv <span style='color:#111;'> 5.91KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_2hidden_25nodes_allfeats_original.asv <span style='color:#111;'> 5.40KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_2hidden_25nodes_allfeats_original.m <span style='color:#111;'> 5.51KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_1hidden_25nodes_allfeats_original.m <span style='color:#111;'> 6.00KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_3hidden_25nodes_allfeats_original.m <span style='color:#111;'> 5.94KB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 428B </span>","children":null,"spread":false}],"spread":true},{"title":"MLP most important features","children":[{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_1hidden_15nodes_15feats_original.m <span style='color:#111;'> 5.06KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_2hidden_13nodes_15feats_original.m <span style='color:#111;'> 5.26KB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_3hidden_13nodes_15feats_original.m <span style='color:#111;'> 5.40KB </span>","children":null,"spread":false},{"title":"credit_default_15_best_features_original.xlsx <span style='color:#111;'> 2.26MB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Training in borderline smote dataset","children":[{"title":"credit_default_brdlSMOTE(15_best_features_original).xlsx <span style='color:#111;'> 4.29MB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_3hidden_13nodes_15feats_brdlsmote.m <span style='color:#111;'> 5.92KB </span>","children":null,"spread":false},{"title":"credit_default_15_best_features_original.xlsx <span style='color:#111;'> 2.26MB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true},{"title":"Training in adasyn dataset","children":[{"title":"MLP_gridsearch_3hidden_13nodes_15feats_adasyn.m <span style='color:#111;'> 5.60KB </span>","children":null,"spread":false},{"title":"credit_default_15_best_features_original.xlsx <span style='color:#111;'> 2.26MB </span>","children":null,"spread":false},{"title":"credit_default_ADASYN(15_best_features_original).xlsx <span style='color:#111;'> 4.23MB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Best SVM - Best MLP","children":[{"title":"Best SVM","children":[{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"credit_default_ADASYN(all_features).xlsx <span style='color:#111;'> 9.06MB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_linear_kernel_allfeats_adasyn.m <span style='color:#111;'> 5.08KB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true},{"title":"Best MLP","children":[{"title":"credit_default_brdlSMOTE(15_best_features_original).xlsx <span style='color:#111;'> 4.29MB </span>","children":null,"spread":false},{"title":"MLP_gridsearch_3hidden_13nodes_15feats_brdlsmote.m <span style='color:#111;'> 7.53KB </span>","children":null,"spread":false},{"title":"credit_default_15_best_features_original.xlsx <span style='color:#111;'> 2.26MB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"All SVM models","children":[{"title":"Training in original dataset","children":[{"title":"SVM all features","children":[{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_polynomial_kernel_allfeats_original.m <span style='color:#111;'> 3.18KB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_linear_kernel_allfeats_original.m <span style='color:#111;'> 3.17KB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_rbf_kernel_allfeats_original.m <span style='color:#111;'> 3.12KB </span>","children":null,"spread":false},{"title":"SVM_baseline_original.m <span style='color:#111;'> 1.72KB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Training in borderline smote dataset","children":[{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"credit_default_brdlSMOTE(all_features).xlsx <span style='color:#111;'> 8.87MB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_linear_kernel_allfeats_brdl_smote.m <span style='color:#111;'> 3.50KB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true},{"title":"Training in adasyn dataset","children":[{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false},{"title":"credit_default_ADASYN(all_features).xlsx <span style='color:#111;'> 9.06MB </span>","children":null,"spread":false},{"title":"SVM_bayesopt_linear_kernel_allfeats_adasyn.m <span style='color:#111;'> 3.62KB </span>","children":null,"spread":false},{"title":"cohens_kappa.m <span style='color:#111;'> 793B </span>","children":null,"spread":false},{"title":"f1_score.m <span style='color:#111;'> 427B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Feature selection - RELIEF","children":[{"title":"credit_default_feature_selection.m <span style='color:#111;'> 1.01KB </span>","children":null,"spread":false},{"title":"credit_default_processed.xlsx <span style='color:#111;'> 4.17MB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Project report.pdf <span style='color:#111;'> 569.97KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]