Large Margin Cellular Piecewise Linear Classifier
Publish place: The first international smart city conference
Publish Year: 1398
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
SMARTCITYC01_170
تاریخ نمایه سازی: 11 اسفند 1398
Abstract:
piecewise linear classifiers have attracted a lot of attention in recent years, because of their simplicity and classification capability. In this paper, a large margin cellular piecewise linear classifier is introduced, called Cell-SVM. The cellular structure of Cell-SVM obtains a piecewise linear decision boundary which handles non-linearly separable data. Unlike the conventional SVM approaches, the proposed method employs multi hyperplanes instead of one in search space and resulting cellular structure addresses some important issues in machine learning such as: multi-modal classes, nonlinear classification, noisy data and outliers, small sample size, multi-class classification and overfitting to training samples. In experiments, we demonstrate significant gains for the well-known benchmark real datasets when compared to the usual multi-class SVM techniques with RBF kernel like OvO SVM, OvA SVM and MC-SVM. Besides, it is shown that the proposed method achieves comparable results to other popular classification methods such as Neural Network and Decision Tree which performs better in general
Keywords:
multi-class classifier , nonlinear classification , piecewise linear , large margin , cellular structure , support vector machine (SVM)
Authors
Neda Azouji
Dep. of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Ashkan Sami
Dep. of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Mohammad Taheri
Dep. of Computer Science and Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran