Enhancing Software Defect Projections Performance by Class Rebalancing
Keywords:
Class Imbalance, Cost sensitive learning, Median absolute deviation, Sampling methods, Software Defect PredictionAbstract
Data is the most precious asset to any tech firm since it has ability to totally revolutionize an industry. However, great availability does not imply perfection; there are numerous issues with the current quality of publicly available datasets. Class imbalance is one such problem, it basically means that one class label appears more frequently than others, and it occurs practically everywhere, such as in medical diagnosis, natural language processing, fraud detection, and so on. This issue is extremely serious since it undermines the potential of existing machine learning models by lowering their ability to identify newer data that has never been seen before. Many strategies have been proposed to address this problem, including oversampling and under sampling. These procedures, however, either overgeneralize or over diversify the data points, making them unproductive. The ensemble technique is another solution with good outcomes but high complexity. However, to address this challenge more effectively, we propose a model that uses mean absolute deviation to find the best features that have the most impact on the outcome, thus lowering dimensionality, and the adaptive synthetic data creation technique to balance the data. The model prioritizes recall over accuracy, which is a crucial parameter when dealing with data imbalances. The findings of this model appear to be quite promising, with an accuracy of nearly 85% and a recall of 89%. The model's AUC curve is 0.93, demonstrating the model's ability to correctly detect positive and negative instances.
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