Conversion of an Imbalanced Financial Data Source to a Wise Set through Sampling - Chen, Songtao (Asian Accounting and Auditing Advancement, 2021)
A lot of literature has explored viable ways to high-dimensional feature space issues. Training classifiers with an imbalanced dataset is difficult, but minimizing class imbalance can improve performance. We use an educationally-biased dataset. The classifier trained with an imbalanced dataset predicts the majority class more than the minority classes (rarely occurring). Accuracy, precision, recall, and f-measure are used to measure classifier performance. First, we explore classification with an imbalanced financial dataset. After class balance, we compare classifier performance using data-level techniques. Both under-samping and oversampling balance financial datasets, but oversampling dominates. Financial data production in many industries has exploded. Organizations want to process acquired financial data to gain decision-making insights. Classification with an imbalanced dataset may give higher accuracy but low precision and recall for the minority class.