基于广义分形插值理论的多尺度分类尺度下推算法 postprint

Abstract: The research of multi-scale data mining mainly applied to space remote sensing image data sets, and conduct scale division based on the resolution or regional segmentation of the images, then analysis knowledge on each scale layer. Recently, there are quite a few learners applied the multi-scale data mining to general data sets, and conduct scale division based on the level theory, concept hierarchy and inclusion degree etc. , study the distribution rule on different scale layers, and then found significant facts. For example, multi-scale association rules, multi-scale clustering. But it has not been involved in the field of the classification mining. This paper defines the concept of generalized fractal interpolation theory, break the situation that limited to the use of the iteration function system(IFS) , and extend the application of the fractal interpolation. Then, a multi-scale classification scaling-down algorithm based on the generalized fractal interpolation theory named MSCSDA (Multi-Scale Classification Scaling-Down Algorithm) is proposed. This paper performs experiments on four UCI benchmark data sets, and one real data set (H province part of the population) . Then analysis the experimental results compare MSCSDA with KNN, Decision Tree and LIBSVM algorithms on different data sets. The experimental results show that the MSCSDA algorithm gives better results in terms of classification than the others.