Name
Abstract | ||
|---|---|---|
This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running several simulations using two dffirent datasets and performing two diferent analysis of the results, we show that the proposed algorithm, named neuro-evolutionary ensemble (NEVE), was 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 obtained by our model with an existing algorithm, Learn++. NSE, in two difirent nonstationary scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1142/S1469026813400026 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
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 | Journal |
Volume | Issue | ISSN |
12 | 4 | 1469-0268 |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
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 |