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Knowledge Discovery and Representation for Fishery Forecasting
conference contribution
posted on 2023-05-23, 05:03 authored by Yuan, H, Yang, H, Chen, YIn 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.
History
Publication title
Proceedings 2010 3rd International Conference on Environmental and Computer SciencePagination
199-202ISBN
978-1-4244-7630-5Department/School
School of Information and Communication TechnologyPublisher
IEEE PressPlace of publication
ChinaEvent title
ICECS: International Conference on Environmental and Computer ScienceEvent Venue
Kunming, ChinaDate of Event (Start Date)
2010-10-17Date of Event (End Date)
2010-10-19Rights statement
Copyright 2010 IEEERepository Status
- Restricted