Minority Oversampling Technique for Imbalanced Data

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Provided by: International Journal of Scientific and Research Publication (IJSRP)
Topic: Data Management
Format: PDF
Today, solving imbalanced problems is difficult task as it contains an unequal distribution of data samples among different classes and poses a challenge to any classifier becoming hard to learn the minority class samples. Unequal distribution of data samples among many classes confuses supervised learning based classifier as it makes challenging to learn minority class samples. Generating synthetic minority class samples tries to balance the sample distribution between minority and majority classes. To handle imbalanced learning problem, proposed work finds minority samples which are difficult to learn and computes Euclidean distance between nearest majority class samples.
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