A Mathematical Model for Resampling Unbalanced Data Set Based on ICA and Improved ICEEMD
In order to improve the effect of processing the unbalanced data sets,this paper put forward a method of researching the resampling mathematical model of unbalanced data sets based on improved ICEEMD-ICA.Firstly,the distribution characteristics of the unbalanced data set was analyzed.And then,the Improved Complementary Ensemble Empirical Mode Decomposition(ICEEMD)and Independent Component Analysis(ICA)were used to decompose the unbalanced data set and thus to remove the noise from it.Secondly,DP clustering algorithm and σ criterion were a-dopted to construct a resampling mathematical model,which could automatically identify the cluster center and outliers of the unbalanced data set and analyze the majority and minority samples at the same time,thus ensuring that the samples were relatively balanced.Finally,the resampling process for the unbalanced data set was completed.The experimental results show that the overall performance of the proposed model is significantly better than other models.
Unbalanced data set(UDS)ResamplingConstruction of mathematical modelClustering algorithm
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