A novel knowledge discovery model for fishery forecasting

Authors:
Hongchun Yuan;Ying Li;Ying Chen
Affiliations:
College of Information Technology, Shanghai Ocean University, Shanghai, China;College of Information Technology, Shanghai Ocean University, Shanghai, China;School of Information Systems, University of Tasmania, Hobart, Tasmania, Australia
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

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Abstract

In the area of ocean fisheries research, a new research interest is to use marine environment factors for fishery forecasting. This paper proposes a novel knowledge discovery model for fishery forecasting that uses the Indian Ocean big-eye tuna fishery as its testing ground. The model employs a 3-step process. Firstly the support vectors can be obtained by training the Support Vector Machine (SVM) with some sample data. Secondly rules can be extracted from the support vectors by the fuzzy classifier. Finally the fishery dynamic knowledge with due consideration of various dynamic factors can be obtained through extension transformation for the conditions and the conduction transformation for the conclusions. This paper is of great significance for enriching fisheries forecasting methods and revealing the formation mechanism of fishing grounds.