A Hybrid Support Vector Machine Algorithm for Big Data Heterogeneity Using Machine Learning

Big data technology has gained attention in all fields, particularly with regard to research and financial institutions. This changed the world tremendously. Researchers scientists are currently working on its applicability different domains such as health care, medicine, stock market, among others. The being generated at an unexpected pace from multiple sources like social media, care contexts, Internet of things have given rise big data. Management processing represent a challenge for researchers scientists, there is heterogeneity ambiguity. Heterogeneity considered be important characteristic analysis heterogeneous very complex task it involves compilation, storage, varied based diverse patterns rules. proposed focused problem introduces hybrid support vector machine (H-SVM) classifier, which uses base. In algorithm, Euclidean overlap metric (HEOM) distance introduced form clusters classify basis ordinal nominal values. performance learning classifier compared linear SVM, random forest, k-nearest neighbor. algorithm attained highest accuracy other classifiers.