Sensitivity and similarity regularization in dynamic selection of ensembles of neural networks
| Autor: | B. Keshavarz-Hedayati, Nikitas J. Dimopoulos |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Basis (linear algebra) business.industry Computer science Pattern recognition 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Regularization (mathematics) Set (abstract data type) 010104 statistics & probability Similarity (network science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Sensitivity (control systems) Artificial intelligence Noise (video) 0101 mathematics business computer Selection (genetic algorithm) |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2017.7966354 |
| Popis: | Dynamic ensemble selection techniques, on a case by case basis, select a subset from the pool of available ensembles to classify the unknown exemplars. This selection process is usually based on a criterion that the selected ensembles should meet. In this paper, first we present a method to improve the performance of the available ensembles by regulating their sensitivity behavior towards the noise perturbations to their respective training sets. Then we present a set of dynamic ensemble selection criteria to evaluate these ensembles based on their similarity and diversity of behavior compared to other ensembles when an unknown case is presented to them. We then present the results of the experiments conducted over several classification data sets (some ill-defined problems are included as well). The results show improvements compared to the state-of-the-art dynamic selection techniques. |
| Databáze: | OpenAIRE |
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