A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine

As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix $\mathbf {\hat {U}}$ decomposition algorithm, and matrix $\mathbf {V}$ decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an $8.71\times$ speedup on a cluster with ten nodes, and reaches a $13.79\times$ speedup with 15 nodes, an $18.74\times$ speedup with 20 nodes, a $23.79\times$ speedup with 25 nodes, a $28.89\times$ speedup with 30 nodes, and a $33.81\times$ speedup with 35 nodes.