Reduction of Training Data Using Parallel Hyperplane for Support Vector Machine

ABSTRACT

Support Vector Machine (SVM) is an efficient machine learning technique applicable to various classification problems due to its robustness. However, its time complexity grows dramatically as the number of training data increases, which makes SVM impractical for large-scale datasets. In this paper, a novel Parallel Hyperplane (PH) scheme is introduced which efficiently omits redundant training data with SVM. In the proposed scheme the PHs are recursively formed while the clusters of data points outside the PHs are removed at each repetition. Computer simulation reveals that the proposed scheme greatly reduces the training time compared to the existing clustering-based reduction scheme and SMO scheme, while allowing the accuracy of classification as high as no data reduction scheme.

Additional information

Funding

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2015-0-00914), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A6A3A11931385, Research of key technologies based on software defined wireless sensor network for real-time public safety service, 2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity), the second Brain Korea 21 PLUS project, and Samsung Electronics.