Domain Knowledge-Based Compaction

Domain knowledge about the problem on hand always leads to an effective solution. In this chapter, we discuss ways to make use of domain knowledge in generating abstraction. We consider binary classifiers such as support vector machine (SVM) and adaptive boosting (AdaBoost) to classify 10-class handwritten digit data. We carry out statistical analysis on the data to derive inferences on domain knowledge. We combine it with human expert’s domain knowledge to arrive at a decision tree of depth 4 to classify 10-class data accurately. In this process, we provide an overview of multiclass classification approaches, decision trees, SVM, and AdaBoost. We combine prototype selection with both these methods to obtain high classification accuracy. In essence, the approach emphasizes exploitation of domain knowledge in mining large datasets, which in the present case results in significant compaction in the data and multiclass classification. We provide a discussion on relevant literature and a list of references at the end of the chapter.