A Positive-Approximation Based Accelerated Algorithm to Feature Selection from Incomplete Decision Tables
Qian Yu · Chinese Journal of Computers · 2011
Positive approximation is an effective approach to characterizing the structure of a target concept in information systems.To overcome the limitation of time-consuming of all existing feature selection algorithms in incomplete decision tables.This paper provides a general accelerated algorithm based on the positive approximation.This modified algorithm both possesses the rank preservation of attributes and reduces the time consumption through reducing the scale of data,which effectively accelerates the process of feature selection in incomplete decision tables.Experimental analyses verify the validity and efficiency of the accelerated algorithm.It is deserved to point out that the performance of these modified algorithms are getting better in time reduction with the data set becoming larger.