Hadoop 平台下粒子滤波结合改进 ABC 算法的 IoT 大数据特征选择方法.

Aiming at the problem that the existing Internet of Things big data feature selection algorithm has low computational efficiency and low scalability, this paper proposed a system architecture that selected features by using improved artificial bee colony. The architecture included a four-layer system and it could efficiently aggregate the effective data and eliminated unwanted data. The entire system was based on the Hadoop platform, MapReduce, and improved ABC algorithm. The method used improved ABC algorithm to select features and it also used a parallel algorithm to support MapReduce, which could efficiently process a huge volume of data sets. It used MapReduce tool to implement the system and used particle filter for removal of noise. The proposed algorithm and similar algorithms were evaluated for the efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed algorithm is more scalable and efficient in selecting features. [ABSTRACT FROM AUTHOR]