Name
Abstract | ||
|---|---|---|
In our previous works [1, 2], we proposed NEVE, a model that uses a weighted ensemble of neural network classifiers for adaptive learning, trained by means of a quantum-inspired evolutionary algorithm (QIEA). We showed that the neuro-evolutionary classifiers were able to learn the dataset and to quickly respond to any drifts on the underlying data. Now, we are particularly interested on analyzing the influence of an unlimited ensemble, instead of the limited ensemble from NEVE. For that, we modified NEVE to work with unlimited ensembles, and we call this new algorithm NEVE++. To verity how the unlimited ensemble influences the results, we used four different datasets with concept drift in order to compare the accuracy of NEVE and NEVE++, using two other existing algorithms as reference. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889820 | Neural Networks |
Keywords | Field | DocType |
evolutionary computation,learning (artificial intelligence),pattern classification,NEVE++,QIEA,adaptive learning,neuro-evolutionary classifiers,neuro-evolutionary unlimited ensemble,quantum-inspired evolutionary algorithm,Adaptive Learning,Concept Drift,Neuro-Evolutionary Ensemble,Nonstationary Environments,Quantum-Inspired Evolution | Interactive evolutionary computation,Evolutionary acquisition of neural topologies,Evolutionary robotics,Pattern recognition,Human-based evolutionary computation,Computer science,Artificial intelligence,Evolutionary programming,Adaptive learning,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
15 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana Escovedo | 1 | 17 | 3.72 |
| Abs da Cruz, A. | 2 | 0 | 0.34 |
| Adriano Soares Koshiyama | 3 | 34 | 10.19 |
| Rubens Melo | 4 | 0 | 0.34 |
| Andre Abs Da Cruz | 5 | 0 | 0.34 |
| Marley Vellasco | 6 | 0 | 0.34 |