Hybrid Support Vector Machine with Grey Wolf Optimization for Classifying Multivariate Data

Rapid improvements in the world of information technology have resulted in a demand for data collecting and the act of storing data for future retrieval. As a result, many businesses and organisations are driven to collect and store massive amounts of data in the data warehouse, a recently built database module. Data classification is a strategy for building a data classifier model that describes the relevant data classes and allows for a better comprehension of the data. It is important to highlight that this data classification approach is not well defined and is a non-deterministic process because there is no guarantee that the data classifier model constructed from past data would work better for future data. In the light of these facts, this chapter discusses the data classification method and its usefulness in the engineering and science fields. The significance and need for a research contribution for data classification using various machine learning models with optimization methods are highlighted, bringing the research work’s aims to light. In this chapter, the various approaches used for designing and developing classifier models, as well as their benefits and drawbacks, are discussed, as well as the hybrid support vector machine with grey wolf optimization (HSVMGWO) used to classify the various datasets. After a thorough evaluation, the proposed HSVMGWO method outperformed the four types of conventional methods: random forest, support vector machine, ada boost algorithm, and finally decision tree, with an efficiency of 95.3 percent, sensitivity of 97.08 percent, specificity of 95.68 percent, and time responsibility of 13.186 M.sec. Statistical study of accuracy values and calculation time shows that the proposed schemes outperform existing approaches.