Optimization and Analysis on Fuzzy SVM for Targets Classification in Forest
Ding Xiaokang*, 1, Yan Lei2, Yu Jianping1, Zhou Zhaozhong1
1 Quzhou University, Quzhou, Zhejiang, China
2 Beijing Forestry University, Beijing, China
Abstract
In this paper, machine learning technology was introduced to forestry machine, and harvesting targets data were classified based on fuzzy support vector machine (FSVM). Fuzzy membership function largely determines the classifier results, so a new membership function was proposed to improve classification performance. Clustering center was selected based on a K-means algorithm, then the membership function was determined via comparing distance between samples to the positive and negative clustering centers in feature space, respectively. It has good learning ability and generalization performance by the experiment with common SVM and representative Fuzzy-SVM. Besides, this model is applied in harvesting target detection, which improves classification accuracy and satisfies with the request of forestry machine.
Keywords: Class imbalance learning, Fuzzy Membership, K-means, SVM, Target Classification.
Article Information
Article History:
Received Date: 14/03/2016
Revision Received Date: 16/07/2016
Acceptance Date: 21/07/2016
Electronic publication date: 06/09/2016
Collection year: 2016
© Xiaokang et al; Licensee Bentham Open.
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (
https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
*
Address correspondence to this author at the Quzhou University, Quzhou, Zhejiang, China; Tel: +86-13967018849; E-mail: dingxiaokang1985@126.com
Open Peer Review Details |
Manuscript submitted on 14-03-2016 |
Original Manuscript |
Optimization and Analysis on Fuzzy SVM for Targets Classification in Forest |