A multi-class IC package type classifier based on kernel-based nonlinear LS-SVM method

DOI

10.1080/18756891.2014.892265How to use a DOI?

Keywords

IC Packaging Type, Feature selection, Least Squares Support Vector Machine, Kernel-Based, Classification Accuracy

Abstract

Least Squares Support Vector Machine (LS-SVM) is powerful to solve problems such as multi-class nonlinear classification. In this study, a LS-SVM with kernel-based was applied to multi-class IC packaging type dataset classification problem. In the first stage, the greedy search algorithm in feature selection that reduced 15 features to 9 features was used to improve the LS-SVM. Then, the hyperparameters of LS-SVM were tuned by using 10-fold cross-validation procedure and a grid search mechanism. This study compared the classification performance of two classifiers, namely the LS-SVM with RBF kernel, LS-SVM with polynomial kernel and NN method, in 63 classes of IC packaging type dataset in the full model and feature redundant model. The results showed that, for the classification problem of multi-class IC packaging type dataset, in the full model and reduced model, the classification performance of LS-SVM with RBF kernel is better than that of LS-SVM with polynomial kernel and NN classifier. The accuracy rates of the two classifiers in the full model were 81.12%, 72.38% and 81.68%, respectively, and 85.29%, 78.43% and 83.65% in the reduced model. In sum, regarding the multi-class IC packaging type classification problem, the classifier using the LS-SVM with RBF of the greedy feature reduced model has the highest classification performance in reducing the dataset complexity, with an accuracy rate at 85.29%.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yung-Hsiang Hung
AU  - Mei-Ling Huang
PY  - 2014
DA  - 2014/06/01
TI  - A multi-class IC package type classifier based on kernel-based nonlinear LS-SVM method
JO  - International Journal of Computational Intelligence Systems
SP  - 472
EP  - 480
VL  - 7
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.892265
DO  - 10.1080/18756891.2014.892265
ID  - Hung2014
ER  -