Support Vector Machines for Data Modeling with Software Engineering Applications

Abstract

support vector machine (SVM)data modelingsoftwareengineering applications This chapter presents the basic principles of support vector machines (SVM) and their construction algorithms from an applications perspective. The chapter is organized into three parts. The first part consists of Sects. 53.2 and 53.3. In Sect. 53.2 we describe the data modeling issues in classification and prediction problems. In Sect. 53.3 we give an overview of a support vector machine (SVM) with an emphasis on its conceptual underpinnings. In the second part, consisting of Sects. 53.4–53.9, we present a detailed discussion of the support vector machine for constructing classification and prediction models. Sections 53.4 and 53.5 describe the basic ideas behind a SVM and are the key sections. Section 53.4 discusses the construction of optimal hyperplane for the simple case of linearly separable patterns and its relationship to the Vapnik–Chervonenkis dimension. A detailed example is used for illustration. The relatively more difficult case of nonseparable patterns is discussed in Sect. 53.5. The use of inner product kernels for nonlinear classifiers is described in Sect. 53.6 and is illustrated via an example. Nonlinear regression is described in Sect. 53.7. The issue of specifying SVM hyperparameters is addressed in Sect. 53.8, and a generic SVM construction flowchart is presented in Sect. 53.9. The third part details two case studies. In Sect. 53.10 we present the results of a detailed analysis of module-level NASA data for developing classification models. In Sect. 53.11, effort data from 75 projects is used to obtain nonlinear prediction models and analyzetheir performance. Section 53.12 presents some concluding remarks, current activities in support vector machines, and some guidelines for further reading.