Abstract:

The paper elaborates the basic definitions and properties of neighborhood rough sets and neighborhood entropy. To avoid losing feature information caused by discretization of continuous attributions while reducing attributions, we present a new algorithm of continuous attributions reduction based on neighborhood entropybased measurement. In the process of expending from core attribute sets to the reduction of attribute sets in neighborhood information system(NIS), neighborhood entropybased measurement is not only concerned with the positive field change of the reduction of attribute sets, but examines the distribution characteristics of the neighborhood equivalence classes of sample space in negative field in the decision attribute partition, which possess the finer granularity in the measurement of neighborhood relationship. Experimental results with UCI standard datasets show that compared with those attributions reduction algorithms based on neighborhood approximation measurement, neighborhood effective information ratio measurement, and neighborhood soft margin measurement, the proposed algorithm can effectively reduce continuous attributions in NIS, and at the same time, it maintains better classification accuracy of the reduction of attribute sets.

Key words: attribute reduction;neighborhood entropybased measurement;core attribute;neighborhood information system;sample space in negative field;classification accuracy