Red tide algae classification using SVM-SNP and semi-supervised FCM

Abstract

In this paper, a novel approach for classifying algal images was presented, which is used in flow-cytometry-based real-time red tide monitoring system. Firstly, an ensemble of support vector machines (SVMs) was trained and the test samples were labeled by them based on the summation of negative probability (SNP). Secondly, those samples most likely mistakenly labeled were picked out and re-labeled by semi-supervised fuzzy c-means (FCM) clustering algorithm. Experiments show that this new method improves the accuracy of algal images classification for the same subject with SVMs of different kernels.