A hybrid evolutionary approach to segmentation of non-stationary signals
Digital Signal Processing
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
| Hi-index | 0.00 |
In this paper, a new method for enhancing the performance of Neural Network ensemble is proposed. The main idea of this method is creating diversity for training artificial neural networks (ANNs) using an interesting method which applies clustering ensemble and Genetic Algorithm. In combinational classifier systems, the more diversity in results of the base classifiers yields to better final performance. Inspiring from the boosting, the diversity of the base classifiers is provided by different train sets for base classifiers. The different train sets are derived from the original train set by adding some of data samples in train set. Finding near optimal sets is implemented using clustering ensemble technique and Genetic Algorithm. Finally, the majority vote fuses the outputs of trained MLPs on the new train sets from population of the last generation of GA. Experimental results demonstrate the strength of proposed method on three different datasets.