Name
Abstract | ||
|---|---|---|
Credit analysis is a real-world classification problem where it is quite common to find datasets with a large amount of noisy data. State-of-the-art classifiers that employ error minimisation techniques, on the other hand, require a long time to converge, in order to achieve robustness. This paper explores ClusWiSARD, a clustering customisation of the WiSARD weightless neural network model, applied to two different credit analysis real-world problems. Experimental evidence shows that ClusWiSARD is very competitive with Support Vector Machine (SVM) w.r.t. accuracy, with the advantage of being capable of online learning. ClusWiSARD outperforms SVM in training time, by two orders of magnitude, and is slightly faster in test time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.06.105 | Neurocomputing |
Keywords | Field | DocType |
Bleaching,ClusWiSARD,Clustering,Concept drifting,Credit assignment,Online learning | Pattern recognition,Computer science,Support vector machine,Robustness (computer science),Minimisation (psychology),Weightless,Artificial intelligence,Cluster analysis,Classifier (linguistics),Artificial neural network,Credit analysis,Machine learning | Journal |
Volume | Issue | ISSN |
183 | C | 0925-2312 |
Citations | PageRank | References |
6 | 0.42 | 20 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Douglas de O. Cardoso | 1 | 35 | 5.19 |
| Danilo S. Carvalho | 2 | 30 | 5.94 |
| Daniel S. F. Alves | 3 | 9 | 1.48 |
| Diego Fonseca Pereira de Souza | 4 | 10 | 1.16 |
| Hugo C. C. Carneiro | 5 | 17 | 2.45 |
| Carlos E. Pedreira | 6 | 60 | 6.51 |
| Priscila M. V. Lima | 7 | 101 | 18.86 |
| Felipe M. G. França | 8 | 249 | 51.12 |