Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction

  title={Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction},
  author={Yuchun Tang and Yanqing Zhang},
  journal={2006 IEEE International Conference on Granular Computing},
for undersampling to minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. Consequently, an accurate and fast classifier can be modeled. GSVM-RU ranks as one of the best solutions in ACM KDDCUP 2004 competition for the extremely imbalanced protein homology prediction. I. INTRODUCTION IGHLY skewed data distribution induces the class imbalance problem that happens, in its simplest form, when there are significantly… CONTINUE READING
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