International Conference on Computer and Knowledge Engineering , 2015-10-29

Title : ( WS-TWSVM: Weighted Structural Twin Support Vector Machine by local and global information )

Authors: Ramin Rezvani , Reza Monsefi ,

Citation: BibTeX | EndNote

Abstract

Recently many researches have published their papers on training a classifier based on the structural information of data. A Structural Twin Support Vector Machine (S-TWSVM) was proposed to introduce and balance all structural information of both intra-class and inter-class into its optimization problems. In fact, this method neither consider the structural information conflicting between clusters of one class nor the noise data points that can influence on the structure of the data distribution. In this paper, we propose a new Weighted Structural Twin Support Vector Machine (WS-TWSVM) by its local and global information. In our proposed method, we use a weighted (rather than a simple) summing of structural information to sufficiently exploit class's distribution information, so that, applying the density information of data points, the effects of the noise points on the data structure can be handled. Thus, our proposed method, WS-TWSVM can fully exploit the prior knowledge more efficiently than S-TWSVM leads to improve the model's generalization capacity. As been shown in the experiments, WS-TWSVM is superior to S-TWSVM in term of classification accuracy.

Keywords

, Density information SVM Structural Twin Support Vector Machine Structural information k, NN
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@inproceedings{paperid:1054787,
author = {Rezvani, Ramin and Monsefi, Reza},
title = {WS-TWSVM: Weighted Structural Twin Support Vector Machine by local and global information},
booktitle = {International Conference on Computer and Knowledge Engineering},
year = {2015},
location = {مشهد, IRAN},
keywords = {Density information SVM Structural Twin Support Vector Machine Structural information k-NN},
}

[Download]

%0 Conference Proceedings
%T WS-TWSVM: Weighted Structural Twin Support Vector Machine by local and global information
%A Rezvani, Ramin
%A Monsefi, Reza
%J International Conference on Computer and Knowledge Engineering
%D 2015

[Download]