High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance
Currently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution...
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doaj-80e0ae9824fa492a969de1f803a4f3a92021-07-02T19:47:42ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/19534611953461High-Performance Machine Learning for Large-Scale Data Classification considering Class ImbalanceYang Liu0Xiang Li1Xianbang Chen2Xi Wang3Huaqiang Li4College of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaState Grid Sichuan Economic Research Institute, Chengdu 610041, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaCurrently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution in the dataset, the class imbalance issue is also gradually highlighted. The traditional machine learning algorithms lack of abilities for handling the aforementioned issues so that the classification efficiency and precision may be significantly impacted. Therefore, this paper presents an improved artificial neural network in enabling the high-performance classification for the imbalanced large volume data. Firstly, the Borderline-SMOTE (synthetic minority oversampling technique) algorithm is employed to balance the training dataset, which potentially aims at improving the training of the back propagation neural network (BPNN), and then, zero-mean, batch-normalization, and rectified linear unit (ReLU) are further employed to optimize the input layer and hidden layers of BPNN. At last, the ensemble learning-based parallelization of the improved BPNN is implemented using the Hadoop framework. Positive conclusions can be summarized according to the experimental results. Benefitting from Borderline-SMOTE, the imbalanced training dataset can be balanced, which improves the training performance and the classification accuracy. The improvements for the input layer and hidden layer also enhance the training performances in terms of convergence. The parallelization and the ensemble learning techniques enable BPNN to implement the high-performance large-scale data classification. The experimental results show the effectiveness of the presented classification algorithm.http://dx.doi.org/10.1155/2020/1953461 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yang Liu Xiang Li Xianbang Chen Xi Wang Huaqiang Li |
spellingShingle |
Yang Liu Xiang Li Xianbang Chen Xi Wang Huaqiang Li High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance Scientific Programming |
author_facet |
Yang Liu Xiang Li Xianbang Chen Xi Wang Huaqiang Li |
author_sort |
Yang Liu |
title |
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance |
title_short |
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance |
title_full |
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance |
title_fullStr |
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance |
title_full_unstemmed |
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance |
title_sort |
high-performance machine learning for large-scale data classification considering class imbalance |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2020-01-01 |
description |
Currently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution in the dataset, the class imbalance issue is also gradually highlighted. The traditional machine learning algorithms lack of abilities for handling the aforementioned issues so that the classification efficiency and precision may be significantly impacted. Therefore, this paper presents an improved artificial neural network in enabling the high-performance classification for the imbalanced large volume data. Firstly, the Borderline-SMOTE (synthetic minority oversampling technique) algorithm is employed to balance the training dataset, which potentially aims at improving the training of the back propagation neural network (BPNN), and then, zero-mean, batch-normalization, and rectified linear unit (ReLU) are further employed to optimize the input layer and hidden layers of BPNN. At last, the ensemble learning-based parallelization of the improved BPNN is implemented using the Hadoop framework. Positive conclusions can be summarized according to the experimental results. Benefitting from Borderline-SMOTE, the imbalanced training dataset can be balanced, which improves the training performance and the classification accuracy. The improvements for the input layer and hidden layer also enhance the training performances in terms of convergence. The parallelization and the ensemble learning techniques enable BPNN to implement the high-performance large-scale data classification. The experimental results show the effectiveness of the presented classification algorithm. |
url |
http://dx.doi.org/10.1155/2020/1953461 |
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