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

Abstract

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 decomposition algorithm, and matrix 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 speedup on a cluster with ten nodes, and reaches a speedup with 15 nodes, an speedup with 20 nodes, a speedup with 25 nodes, a speedup with 30 nodes, and a speedup with 35 nodes.

The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

“…The execution of proposed enormous information order conspire in sparkle engineering will be done in JAVA and the dataset used for the examination will be the UCI AI store [26]. The proposed RCBO-based profound learning for huge information grouping is assessed by utilizing the measurements, for example, exactness and shakers coefficient that will be contrasted and the current works [2], [4], and [9]. Figure 1.…”

“…A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine [2] by M. Duan proposed a philosophy a proficient ELM dependent on the Spark system (SELM). The creator proposed a SELM -Spark Extreme Machine Learning which parts the informational index sensibly, which makes numerous calculations to be performed locally as could be allowed and brings down the correspondence cost and I/O cost.…”

“…The term information mining alludes to the non-trifling extraction of legitimate, certain, conceivably valuable and at last reasonable data in huge databases with assistance of the rising registering advancements [7] [6]. As informational indexes become bigger and progressively confused, an outrageous learning machine (ELM) that keeps running in a customary sequential condition can't understand its capacity to be quick and successful [2]. Viable Big Data Mining requires adaptable and proficient arrangements that are additionally effectively available to clients at all degrees of mastery [20].…”

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

“…It is one kind of simple feasible fast learning algorithm. Compared with other traditional neural network learning algorithms or support vector machine (SVM), least square support vector machine (LSSVM), etc, ELM algorithm has the advantages of fast learning speed and strong generalization ability [7,41]. The authors pointed out that computing time of ELM is usually several thousand times faster than BP neural network or SVM [37].…”

Automatically Detecting Excavator Anomalies Based on Machine Learning

“…Based on the real excavator data, we have carried out a comprehensive evaluation. The results show that the anomaly detection accuracy is as high as 99.88%, which is obviously superior to the previous methods based on expert systems and traditional statistical models.Symmetry 2019, 11, 957 2 of 18 condition data increases, it is hard to extract regular patterns from mass of data based on traditional statistic models.The recent advancement of neural network and machine learning (ML) have been successfully applied to various application scenarios [8][9][10][11][12][13][14]. However, none has used them in anomaly detection for excavators.…”