کنفرانس مهندسی برق ایران

صفحه اصلی / سی امین کنفرانس بین المللی مهندسی برق

A boosting based approach to handle imbalanced data

نویسندگان :

Sahar Hassanzadeh Mostafaei¹ Jafar Tanha² Negin Samadi³ Soodabeh Imanzadeh⁴ Nazila Razzaghi-Asl⁵

1- دانشگاه تبریز 2- دانشگاه تبریز 3- دانشگاه تبریز 4- دانشگاه تبریز 5- دانشگاه تبریز

کلمات کلیدی :

Imbalanced data،imbalanced classification،class imbalanced problem،Over-sampling،Under-sampling،Imbalance ratio،boosting

چکیده :

Most real-world datasets usually contain imbalanced data. Learning from datasets where the number of instances in one class (minority) is much smaller than in another class (majority) creates biased classifiers to the majority class. The overall prediction accuracy in imbalanced datasets is higher than 90%, while this accuracy is relatively lower for minority classes. In this paper, we first propose a new technique for under-sampling based on the Peak clustering method from majority class on imbalanced datasets. We then, propose a novel boosting-based algorithm for learning from imbalanced datasets, based on a combination of the proposed Peak under-sampling algorithm and over-sampling technique (SMOTE) in the boosting procedure. In the proposed algorithm misclassified examples are not given equal weights. The proposed algorithm selects useful samples from the majority class and creates synthetic samples from the minority class to indirectly change the update weights. We designed experiments using 10 datasets from various domains and different evaluation metrics that show improvement in prediction efficiency, on the minority class.