Broaden the minority class space for decision tree induction using antigen-derived detectors

This paper focuses on broadening the minority space by learning both majority class space and minority class space.A negative selection over-sampling technology (NSOTE) is proposed.Previous over-sampling methods only learn minority class space to produce minority class examples.We also investigate the performance of NSOTE and previous over-sampling methods on artificial and real datasets. To deal with lack of density over imbalanced datasets, a Negative Selection Over-Sampling Technology (NSOTE) is proposed. NSOTE is based on a negative selection mechanism of our human immune system. It generates antigen-derived detectors of majority class examples to enrich the decision regions of the space of minority class. Meanwhile, through learning the density distribution of minority class examples, NSOTE eliminates the noise detectors that deviate from the minority class space. Our experimental results show that our NSOTE can achieve better performance than existing resampling methods.

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