Name
Title | ||
|---|---|---|
Data classification with binary response through the Boosting algorithm and logistic regression. |
Abstract | ||
|---|---|---|
•Review of AIC and BIC information criteria focused on binary data classification.•Usual data classification is presented with its drawbacks (i.e., low performance).•Boosting algorithm showed enhanced results supported by MC simulation.•Hosmer–Lemeshow test sets the partition of the training(test) for classification.•CHD disease classification is performed with Boosting showing its high performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.eswa.2016.08.014 | Expert Systems with Applications |
Keywords | Field | DocType |
Boosting algorithm,Data classification,Logistic regression,Information criteria,AIC,BIC,Selection of models,Monte Carlo Simulation | Interpretability,Pattern recognition,Binary classification,Information Criteria,Computer science,Logistic model tree,Artificial intelligence,Boosting (machine learning),Data classification,Logistic regression,Machine learning,False positive paradox | Journal |
Volume | ISSN | Citations |
69 | 0957-4174 | 4 |
PageRank | References | Authors |
0.53 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fortunato S. De Menezes | 1 | 9 | 2.00 |
| Gilberto R. Liska | 2 | 4 | 0.53 |
| marcelo ângelo cirillo | 3 | 4 | 1.55 |
| Mário J. F. Vivanco | 4 | 4 | 0.53 |