Neural Network Ensemble Based on Rough Sets Reduction and its Application to Remote Sensing Image Classification
Dongbo Zhang · 2008
Neural network ensemble based on rough sets reduction is proposed to decrease the computing complexity of conventional feature ensemble selection algorithms.Firstly,a dynamic reduction technology,which integrates genetic algorithm and resample method,is used to get reduct sets that have stable and good generalization ability.Secondly,Multiple BP neural networks based on different reducts are built as base classifiers.According to the idea of selective ensemble,the best generalization ability neural network ensemble can be found by some search strategies.Finally,classification based on neural network ensemble can be completed by combination with vote rule.The method has been verified in the experiment of classifying Landsat 7 bands remote sensing image of chosen area.A number of feature sets of poor performance were discarded by reduction based on rough sets.Compared to conventional feature selection algorithms,the method takes less time,has lower computing complexity,and the performance is satisfying.