Algorithm-Level Approaches

Algorithm-level solutions can be seen as an alternative approach to data pre-processing methods for handling imbalanced datasets. Instead of focusing on modifying the training set in order to combat class skew, this approach aims at modifying the classifier learning procedure itself. This requires an in-depth understanding of the selected earning approach in order to identify what specific mechanism may be responsible for creating the bias towards the majority class. Algorithm-level solutions do not cause any shifts in data distributions, being more adaptable to various types of imbalanced datasets – at the cost of being specific only for a given classifier type. In this chapter we will discuss the basics of algorithm-level solutions, as well as review existing skew-insensitive modifications. To do so, the background will be introduced first in Sect. 6.1. Then, special attention will be given to four groups of methods. First, modifications of SVMs will be discussed in Sect. 6.2. Section 6.3 will focus on skew-insensitive decision trees. Variants of NN classifiers for imbalanced problems will be presented in Sect. 6.4 and skew insensitive Bayesian in Sect. 6.5. Finally, one-class classifiers will be discussed in Sect. 6.6, whereas Sect. 6.7 will conclude this chapter and will present future challenges in the field of algorithm-level solutions to class imbalance.