On parameters optimization of dynamic weighted majority algorithm based on genetic algorithm

Dynamic weighted majority-Winnow (DWM-WIN) algorithm of [5] is a powerful classification method for nonstationary environments which copes with concept drifting data streams. DWM-WIN parameters setting in a training process impacts on the classification accuracy. Unfortunately, these parameters are randomly chosen and without any rational selection. The objective of this research study is to optimize the choice of these parameters. We use genetic algorithm (GA) of [6] as an optimization method in order to dynamically search for the best parameter values of DWM-WIN and improve the classification accuracy. To assess this optimized DWM-WIN algorithm, DWMWIN is used as a fitness function in the GA. Based on 4 datasets from UCI data sets repository, simulations have shown that the proposed DWM-WIN-GA outperforms existing classification methods.