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

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》

吴颖 李晓玲 唐晶磊

Abstract： 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 selects features by using improved artificial bee colony. The architecture included a four-layer system and it could efficiently aggregate the effective data and eliminate unwanted data. The entire system was based on the Hadoop platform, MapReduce, and improved ABC algorithms. 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 it used particle filter for removal of noise. Compare the proposed algorithm with similar algorithms and evaluate 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.

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