Research Review on Imbalanced Learning Models in Embedded Intelligent Computing

Imbalanced data sets are a common phenomenon in embedded intelligent computing. In other words, the class imbalance occurs when the number of examples representing one class is much lower than the ones of the other classes. Hence, one or more classes may be underrepresented in the dataset. However, standard machine learning algorithms are usually biased toward the majority class, since rules correctly predicting those instances are positively weighted in favor of the accuracy metric or the corresponding cost function. As a consequence, minority class instances are more often misclassified than those from the majority one. One of the main issues in imbalanced problems is that usually, the underrepresented class is the class of interest of the problem from the application point of view. Therefore, there are a large number of researches on imbalanced learning. Compared with the binary class imbalanced problem, the multi-class imbalanced problem faces greater challenges, which is attributed to the diversity of class distribution and the insufficient performance of multi-class classifiers. Therefore, imbalanced problems have received more and more attention in recent years. This paper studies the mainstream imbalance algorithms, classifies the current models and algorithms to solve the imbalance problem, analyzes the advantages and disadvantages of various methods, discusses the performance indicators suitable for imbalance problems and their evaluation bias, and summarizes the research trend of algorithms.