A granular ensemble learning based on multi-sampling approximate granulation
Granulation is a method to construct the granular data and granular models.In recent years,several granulation methods have been proposed.For instance,similarity granulation based on sample similarity scale,neighborhood granulation derived from neighborhood relationship,rotation granulation based on feature transformation,and so forth,have demonstrated outstanding performance in supervised and unsupervised tasks.Nevertheless,these granulation techniques are formulated on the metric associations of the samples themselves,which result in varying extents of information expansion during the granulation process.This property renders the granules challenging to manage in certain cases.An approach to construct approximate granules using a multi-sampling method is proposed in this paper.This method guarantees a finite amount of granulation.Furthermore,the fixed metric relation is discarded in the granulation process,causing the granules to vary with the chosen approximation set and approximation base model.This variation increases the flexibility of samples in granulation to granules.We present a comprehensive comparison of multi-sampling approximate granulation with multiple granulation methods.The results demonstrate that multi-sampling approximate granulation outperforms other methods in terms of classification performance.Furthermore,we conduct a thorough comparison with various advanced ensemble algorithms,the final results indicate that the granular ensemble model exhibits superior robustness and generalization for classification tasks.
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