Fuzzy case-based reasoning: weather prediction
Kan Li, Yushu Liu · 2003
The classical K-nearest neighbor (K-nn) algorithm in case-based reasoning (CBR) has been used widely. But in real situations, cases often have kinds of features that the classical K-nn algorithm cannot tackle well. Other fuzzy K-nn algorithms may apply well to these perspective systems, but do not adapt to weather prediction. In this paper, we propose a novel fuzzy K-nn algorithm. Because weather is continuous, dynamic and chaotic, in our algorithm, the time function as an adjustable factor is introduced to the similarity-measuring function. Fuzzy logic is used in the retrieval of cases. Experimental results show the efficacy of the algorithm.