Feature reduction based on interpolated existential null values
Huang Feng-gang · Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University · 2010
In multi-source information fusion,feature reduction is an accepted and effective way to eliminate redundant data and enhance the precision of fusion.Facing an incomplete information system containing sensor data with a multi-source heterogeneous structure,First,variety of analytical methods for calculating distances were used to determine the interval distribution of existing null values.Second,a limited tolerance relation based on interpolating existing null values was proposed for this incomplete information system.It has two kinds of null value types: an existing null value type and an unassigned value type.Next,by combining the interdependent relation of knowledge included in features,the heuristic algorithm for reducing the importance of feature attributes was improved by introducing the concept of knowledge granularity.Subsequently,performance of the algorithm was validated in an experiment.A variety of types of null values were taken into account,as they were more suitable for the features of multi-source heterogeneous sensor data.This eliminated oversimplifications of non-existent or missing null values which then cause inaccurate reductions.Compared with other reduction algorithms,the proposed heuristic algorithm based on knowledge granularity not only considers the filling of a single value,but also the possible discrete set of interpolation values.Both techniques can increase the adaptability of the reduction algorithm.