Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce

Abstract

In this era of big data, analyzing large scale data efficiently and accurately has become a challenge problem. Online sequential extreme learning machine is one of ELM variants, which provides a method to analyze data. Ensemble method provides a way to learn data more accurately. MapReduce provides a simple, scalable and fault-tolerant framework, which can be utilized for large scale learning. In this paper, we propose an ensemble OS-ELM framework which supports ensemble methods including Bagging, subspace partitioning and cross validating. Further we design a parallel ensemble of online sequential extreme learning machine (PEOS-ELM) algorithm based on MapReduce for large scale learning. PEOS-ELM algorithm is evaluated with real and synthetic data with the maximum number of training data 5120K and the maximum number of attributes 512. The speedup of this algorithm can reach as high as 40 on a cluster with maximum 80 cores. The accuracy of PEOS-ELM algorithm is at the same level as that of ensemble OS-ELM running on a single machine, which is higher than that of the original OS-ELM.