 | 2008 |
|---|
| 12 |       | Ana Carolina Lorena,
Marinez F. de Siqueira,
Renato De Giovanni,
André Carlos Ponce Leon Ferreira de Carvalho,
Ronaldo C. Prati:
Potential Distribution Modelling Using Machine Learning.
IEA/AIE 2008: 255-264 |
| 11 | | Ronaldo C. Prati,
Gustavo E. A. P. A. Batista,
Maria Carolina Monard:
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System.
IFIP AI 2008: 131-140 |
| 10 | | Edson Takashi Matsubara,
Ronaldo C. Prati,
Gustavo E. A. P. A. Batista,
Maria Carolina Monard:
Missing Value Imputation Using a Semi-supervised Rank Aggregation Approach.
SBIA 2008: 217-226 |
| 2006 |
|---|
| 9 | | Adriano D. Pila,
Rafael Giusti,
Ronaldo C. Prati,
Maria Carolina Monard:
A Multi-Objective Evolutionary Algorithm to Build Knowledge Classification Rules with Specific Properties.
HIS 2006: 41 |
| 8 | | Edson Takashi Matsubara,
Maria Carolina Monard,
Ronaldo C. Prati:
On the Class Distribution Labelling Step Sensitivity of CO-TRAINING.
IFIP AI 2006: 199-208 |
| 7 | | Flavia Cristina Bernardini,
Maria Carolina Monard,
Ronaldo C. Prati:
Constructing ensembles of symbolic classifiers.
Int. J. Hybrid Intell. Syst. 3(3): 159-167 (2006) |
| 2005 |
|---|
| 6 | | Flavia Cristina Bernardini,
Maria Carolina Monard,
Ronaldo C. Prati:
Constructing Ensembles of Symbolic Classifiers.
HIS 2005: 315-322 |
| 5 | | Gustavo E. A. P. A. Batista,
Ronaldo C. Prati,
Maria Carolina Monard:
Balancing Strategies and Class Overlapping.
IDA 2005: 24-35 |
| 4 | | Ronaldo C. Prati,
Peter A. Flach:
ROCCER: An Algorithm for Rule Learning Based on ROC Analysis.
IJCAI 2005: 823-828 |
| 2004 |
|---|
| 3 | | Ronaldo C. Prati,
Gustavo E. A. P. A. Batista,
Maria Carolina Monard:
Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior.
MICAI 2004: 312-321 |
| 2 | | Ronaldo C. Prati,
Gustavo E. A. P. A. Batista,
Maria Carolina Monard:
Learning with Class Skews and Small Disjuncts.
SBIA 2004: 296-306 |
| 1 | | Gustavo E. A. P. A. Batista,
Ronaldo C. Prati,
Maria Carolina Monard:
A study of the behavior of several methods for balancing machine learning training data.
SIGKDD Explorations 6(1): 20-29 (2004) |
