Attribute reduction method using Water-Filling principle for Case-based reasoning

As the large number of feature attributes in Case-based reasoning system (CBR) brings a huge information redundancy which reduces the retrieval efficiency, a novel reduction method based on Water-Filling is proposed to remove those unnecessary attributes. In the method, the importance of each attribute could be calculated by utilizing the ratio of the standard deviation and the mean value of each attribute data as evaluation parameter, and the impotance result of each attribute is then used to guide the reduction process. The experiments on glass identification showed that the new method could get a better retrieval accuracy as well as a greater efficiency compared with the methods which do not conduct the reduction process.