Optimization and analysis on fuzzy SVM for targets classification in forest

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.