Name
Abstract | ||
|---|---|---|
This work describes the use of a quantum-inspired evolutionary algorithm (QIEA-R) to construct a weighted ensemble of neural network classifiers for adaptive learning in concept drift problems. The proposed algorithm, named NEVE (meaning Neuro-EVolutionary Ensemble), uses the QIEA-R to train the neural networks and also to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running eight simulations using two different datasets and performing two different analysis of the results, we show that NEVE is able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results reached by our model with an existing algorithm, Learn++. NSE, in two different nonstationary scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41142-7_64 | ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2013 |
Keywords | Field | DocType |
adaptive learning, concept drift, neuro-evolutionary ensemble, quantum-inspired evolution | Evolutionary algorithm,Computer science,Concept drift,Artificial intelligence,Artificial neural network,Classifier (linguistics),Adaptive learning,Ensemble learning,Machine learning | Conference |
Volume | ISSN | Citations |
412 | 1868-4238 | 2 |
PageRank | References | Authors |
0.39 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana Escovedo | 1 | 17 | 3.72 |
| André Vargas Abs da Cruz | 2 | 29 | 5.91 |
| Marley Maria Bernardes Rebuzzi Vellasco | 3 | 66 | 11.93 |
| Adriano Soares Koshiyama | 4 | 34 | 10.19 |