Performance Evaluation of Kernel SVM on Sparse Datasets with Large Attributes

  • Chamila Walgampaya
  • Modestus Lorence
Keywords: Support Vector Machines, Kernels, sparse data, large attributes, Machine Learning

Abstract

Support Vector Machines (SVM) is a Machine Learning Algorithm which is used for Classification and Regression in many applications. The vital characteristic of SVM is that the classification decision function is formulated using very few points in the training dataset. We have provided the less publicized mathematical formulation of Hard Margin SVM Classifier, Soft Margin SVM Classifier and the Kernel Trick. In this paper we have used two sparse data sets, we found that Kernel SVM shows significantly better generalization and prediction accuracy for sparse datasets. We have compared the classification performance with other Machine Learning algorithms such as Logistic Regression, Neural Networks, Bayesian Network, KNN, Bagging and Random Forest.

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Published
2020-03-26
How to Cite
Walgampaya, C., & Lorence, M. (2020). Performance Evaluation of Kernel SVM on Sparse Datasets with Large Attributes. Asian Journal For Convergence In Technology (AJCT), 5(3), 27-33. Retrieved from http://asianssr.org/index.php/ajct/article/view/910