NDLI: Application of improved core vector machine in the prediction of algal blooms in Tolo Harbour

Content Provider	IEEE Xplore Digital Library
Author	Xiu Li Jin Yu Zhuo Jia Huimin Wang
Copyright Year	2015
Description	Author affiliation: Shenzhen Key Lab. of Inf. Sci. & Technol., Shenzhen Tsinghua Univ., Shenzhen, China (Xiu Li; Jin Yu; Zhuo Jia; Huimin Wang)
Abstract	Support vector machine (SVM) and its derivative algorithms have been increasingly used to predict algal blooms recently. However, its computation complexity remains an annoying problem. To improve the time cost of SVM, a hybrid approach is proposed in this paper based on Partial Least Square (PLS) feature extraction and Core Vector Machine Regression (CVR) algorithm. We describe the principle of our algorithm and the implementation steps in detail. Based on the biweekly data gathered from Tolo Harbour, Hong Kong, some comparative analysis of the performance of PLS-CVR and other algorithms are presented. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably. The experimental results show that our algorithm can reduce the time cost significantly compared to conventional SVM algorithm while maintaining a satisfactory accuracy.
Sponsorship	IEEE Comput.Soc.
Starting Page	1
Ending Page	6
File Size	505547
Page Count	6
File Format	PDF
ISBN	9781479986767
DOI	10.1109/SNPD.2015.7176177
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-01
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy support vector machine Biological system modeling algal blooms prediction core vector machine regression algorithm Predictive models Lead Prediction algorithms Feature extraction partial least square
Content Type	Text
Resource Type	Article

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