Distributed Extreme Learning Machine with kernels based on MapReduce

Bi, Xin; Zhao, Xibin; Wang, Guoren; Zhang, Pan; Wang, Chao

doi:10.1016/j.neucom.2014.01.070

Cited by 47 publications

(21 citation statements)

References 23 publications

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“…ELM trains extremely fast and has good generalization. ELM has been successfully applied for pattern recognition, image classification, fault diagnosis, big data analytics, and machine learning [6][7][8][9]. ELM has been effectively used for distributed applications parallel computation-based problems [10][11][12][13].…”

Section: Extreme Learning Machinementioning

confidence: 99%

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Deepa¹,

Rajalakshmi²,

Nedunchezhian³

2017

Data-Enabled Discov. Appl.

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Respiratory diseases are emerging as the major health problem across India and Delhi tops in lung diseaserelated issues. The main reason for such rise in respiratory problems in Delhi is air pollution. Among air pollutants, particulate matter (PM 2.5 ) is very hazardous and it is instrumental in causing lung-related diseases. In this paper, an extreme learning machine (ELM) based on statistically controlled activation weight initialization is used to learn and measure the correlation between PM 2.5 and lung-related problems in Delhi. ELM was trained and tested with PM and lung functionality-related medical data. PM 2.5 level data of different areas of Delhi are collected during January 2016 to December 2016. Lung function-related medical data are collected from reputed hospitals in Delhi and analyzed. Results of sputum sample test and spirometry tests conducted on both adults and school children was collected. The test results for both adults and school children are analyzed, and correlation coefficients are evaluated using linear analysis and Spearman's analysis. The correlation results were positive and thus proving that the increase in lung-related diseases in Delhi is directly related to rising PM 2.5 level in the city.

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Section: Extreme Learning Machinementioning

confidence: 99%

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Deepa¹,

Rajalakshmi²,

Nedunchezhian³

2017

Data-Enabled Discov. Appl.

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“…Bi et al have been proposed both distributed and kernelized ELM (DK-ELM) based on MapReduce [18]. The difference between ELM and Kernelized ELM is that K-ELM applies kernels opposite to create random feature mappings.…”

Section: Distributed and Kernelized Elm: Dk-elmmentioning

confidence: 99%

Classification with boosting of extreme learning machine over arbitrarily partitioned data

Çatak

2015

Soft Comput

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Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels of data availability (i.e. Big Data) automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) to build a predictive bag of classification models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is used to build weak learners (classifier functions); and (iii) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.

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“…Compared with I-ELM, EI-ELM, EM-ELM, and SVR, D-ELM obtained on sigmoid type of hidden nodes show a good job on reducing the network size while preserving good generalization performance. There are also a lot of improved ELM algorithms and application of ELM [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A fast training algorithm for extreme learning machine based on matrix decomposition

Chen

Tang

et al. 2016

Neurocomputing

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Distributed Extreme Learning Machine with kernels based on MapReduce

Cited by 47 publications

References 23 publications

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Impact of Air Pollution on Respiratory Diseases: Correlation and Classification by Multivariate Data Analysis

Classification with boosting of extreme learning machine over arbitrarily partitioned data

A fast training algorithm for extreme learning machine based on matrix decomposition

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