Cloud Model based classifier

Cloud Model is a well-known model of the uncertainty transition between a linguistic term of a qualitative concept and its numerical representation. Samples to be classified generally contain many features. Different features have different importance, which are often classified by weights. For the same category, feature vectors were mapped into clouds. With different numerical characters of the clouds, we could get the cloud similarities and feature weights. The testing samples´ contribution to a certain class was measured by the certainty degree of Cloud Model. We proposed a new classification algorithm based on Could Model. Experiments show that such an approach could achieve a better or at least a comparable classification accuracy with other algorithms.