Density ratio estimation in support vector machine for better generalization: study on direct marketing prediction

Authors:
Muhammad Syafiq Mohd Pozi;Aida Mustapha;Anas Daud
Affiliations:
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia;Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
Venue:
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2013

Citing 4
Cited 0

A Least-squares Approach to Direct Importance Estimation

The Journal of Machine Learning Research
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
A unifying view on dataset shift in classification

Pattern Recognition
Multi-parametric solution-path algorithm for instance-weighted support vector machines

Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we show how to improve the generalization performance of Support Vector Machine (SVM) by incorporating density ratio based on Unconstrained Least Square Importance Fitting (uLSIF) into the SVM classifier. ULSIF function is known to have optimal non-parametric convergence rate with optimal numerical stability and higher robustness. The ULSIF-SVM classifier is validated using marketing dataset and achieved better generalization performance as compared against classic implementation of SVM.