Fuzzy clustering and decision tree learning for time-series tidal data classification
Document Type
Conference Proceeding
Publication Date
7-11-2003
Abstract
In this paper, a hybrid decision tree learning approach is presented that combines fuzzy C-means method and the ID3 algorithm in decision tree construction from continuous-valued features. The fuzzy C-means method is applied to find a number of central means for each continuous-valued feature and thus discretize such features. The ID3 algorithm is subsequently used to build a decision tree from the discretized data. Preliminary experiments using a real-world time-series data set from the Louisiana coast are reported that compare our method with the OC1 system for oblique decision tree learning. The experiment results seem to suggest that the proposed hybrid method achieves better or comparable classification accuracy.
Publication Source (Journal or Book title)
IEEE International Conference on Fuzzy Systems
First Page
732
Last Page
737
Recommended Citation
Chen, J., Chen, J., & Kemp, G. (2003). Fuzzy clustering and decision tree learning for time-series tidal data classification. IEEE International Conference on Fuzzy Systems, 1, 732-737. Retrieved from https://repository.lsu.edu/eecs_pubs/2414
