A synthetic algorithm of advantaged boundary for minority class samples based on weight-ed distance is presented to overcome the issue of class imbalance in data set.The ABWD algorithm has three characteristics:first,it defines a weighted distance metric and selects sample neighbors based on this distance.Second,it determines whether a sample belongs to the minority class's bound-ary based on its proximity to other samples.Finally,it calculates the positions and quantities of syn-thetic samples for each boundary sample within the minority class,ensuring that the number of mi-nority class samples is not less than that of the majority class in the neighborhood after synthesis.This guarantees an advantaged boundary for the minority class samples.Experimental results demon-strate that the proposed algorithm significantly enhances the classification performance of the minori-ty class when compared to other typical oversampling techniques.Good experimental results are ob-tained on G-mean,F-measure and recall.
data miningimbalanced dataoversamplingadvantaged boundaryweighted distance
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