Sensor data reduction with novel local neighborhood information granularity and rough set approach

Abstract

Data description and data reduction are important issues in sensors data acquisition and rough sets based models can be applied in sensors data acquisition. Data description by rough set theory relies on information granularity, approximation methods and attribute reduction. The distribution of actual data is complex and changeable. The current model lacks the ability to distinguish different data areas leading to decision-making errors. Based on the above, this paper proposes a neighborhood decision rough set based on justifiable granularity. Firstly, the rough affiliation of the data points in different cases is given separately according to the samples in the neighborhood. Secondly, the original labels are rectified using pseudo-labels obtained from the label noise data that has been found. The new judgment criteria are proposed based on justifiable granularity, and the optimal neighborhood radius is optimized by the particle swarm algorithm. Finally, attribute reduction is performed on the basis of risky decision cost. Complex data can be effectively handled by the method, as evidenced by the experimental results.