An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number multi-label samples are available in our daily life. As result, classification has aroused widespread concern. Different traditional machine learning methods which time-consuming during training phase, ELM-RBF (extreme machine-radial basis function) is more efficient and become research hotspot classification. However, because lack effective optimization methods, conventional extreme machines always unstable tend to fall into local optimum, leads low prediction accuracy practical applications. To this end, modified with synergistic adaptive genetic algorithm (ELM-RBF-SAGA) proposed paper. In ELM-RBF-SAGA, we present (SAGA) optimize performance ELM-RBF. addition, two employed collaboratively SAGA. One used for adjusting range fitness value, other applied update crossover mutation probability. Sufficient experiments show that ELM-RBF-SAGA excellent