Abstract
Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction.
Highlights
Introduction on Subgroup Migration and AdaptiveIn machine learning applications, the class imbalance problem refers to the large difference in the number of different datasets, namely, one class has more samples than the other
We propose a Particle Swarm Optimization algorithm based on Subgroup Migration (SMPSO), which improves the evolutionary strategy of particle swarm optimization (PSO)
SMPSO can effectively improve the performance of software defect prediction
Summary
The class imbalance problem refers to the large difference in the number of different datasets, namely, one class has more samples than the other. Classic classification algorithms such as decision tree, Bayesian algorithms, Support Vector Machine (SVM) and neural networks are widely used in software defect prediction. Traditional algorithms assume that a data set is balanced and a focus on achieving the best overall classification accuracy. When a traditional classification algorithm is applied to an imbalanced data set, it can misclassify a large number of minority class samples into the majority class. Because the classification accuracy of high-risk modules is more critical than the overall classification accuracy, the minority class can be more important than the majority class
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