eCite Digital Repository

Knowledge Discovery and Representation for Fishery Forecasting


Yuan, H and Yang, H and Chen, Y, Knowledge Discovery and Representation for Fishery Forecasting, Proceedings 2010 3rd International Conference on Environmental and Computer Science, 17-19 October, 2010, Kunming, China, pp. 199-202. ISBN 978-1-4244-7630-5 (2010) [Refereed Conference Paper]

Copyright Statement

Copyright 2010 IEEE

Official URL:


In the marine industry there has always been immense research interest in maximizing accuracy of fishery forecasting. The fishery knowledge have a great impact on the accuracy, so this paper proposes a new knowledge discovery and representation model for obtaining and representing fishery knowledge, which takes a 3 step process. Firstly, it extracts static knowledge from database by SVM classifier and fuzzy classifier. Secondly, it uses extension data mining method to transfer static knowledge into dynamic knowledge. Thirdly, it establishes an ontology knowledge base by utilizing a mapping mechanism between the dynamic knowledge and ontology. Using the proposed model building procedure, the authors implemented a prototype system for fishery forecasting. Experimental results show that the proposed method is effective and efficient.

Item Details

Item Type:Refereed Conference Paper
Keywords:knowledge discovery; extension data mining; ontology; fishery forecasting
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Electronic information storage and retrieval services
UTAS Author:Chen, Y (Ms Ying Chen)
ID Code:66482
Year Published:2010
Deposited By:Information and Communication Technology
Deposited On:2011-01-28
Last Modified:2014-12-09
Downloads:1 View Download Statistics

Repository Staff Only: item control page