Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine

Chau Pham; Vung Pham; Tommy Dang

doi:10.1109/BigData47090.2019.9006542

Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine

Chau Pham, Vung Pham, Tommy Dang

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5844-5853
Number of pages	10
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006542
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Keywords

Ensemble framework
Gradient boosting decision tree
Logistic regression
Long Short-term memory neural network
Multilayer perceptron neural network
Multivariate time series
Solar flare prediction

Access to Document

10.1109/BigData47090.2019.9006542

Link to publication in Scopus

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Pham, C., Pham, V., & Dang, T. (2019). Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 5844-5853). [9006542] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006542

Pham, Chau ; Pham, Vung ; Dang, Tommy. / Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5844-5853 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

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title = "Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine",

abstract = "This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).",

keywords = "Ensemble framework, Gradient boosting decision tree, Logistic regression, Long Short-term memory neural network, Multilayer perceptron neural network, Multivariate time series, Solar flare prediction",

author = "Chau Pham and Vung Pham and Tommy Dang",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006542",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5844--5853",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

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Pham, C, Pham, V & Dang, T 2019, Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006542, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 5844-5853, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006542

Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine. / Pham, Chau; Pham, Vung; Dang, Tommy.

Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5844-5853 9006542 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine

AU - Pham, Chau

AU - Pham, Vung

AU - Dang, Tommy

PY - 2019/12

Y1 - 2019/12

N2 - This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).

AB - This paper describes a machine learning approach to the solar flare prediction competition, a track in IEEE Big Data 2019 Big Data Cup. The competition task is to predict whether or not there is a solar flare event basing on a given time series of solar magnetic field parameters. Our method involves exploring and constructing data-driven machine learning models for the classification task of two imbalanced class labels from time series. Specifically, the investigated models include boosting, logistic regression, multilayer perceptron neural network, and long short-term memory neural network. These models have been successfully deployed and combined in an ensemble framework with two tiers in our final proposed solution for this competition. Our proposed approach ranked at the second place in the competition (the first on the private board and the eleventh on the public board).

KW - Ensemble framework

KW - Gradient boosting decision tree

KW - Logistic regression

KW - Long Short-term memory neural network

KW - Multilayer perceptron neural network

KW - Multivariate time series

KW - Solar flare prediction

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M3 - Conference contribution

AN - SCOPUS:85081374683

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 5844

EP - 5853

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 December 2019 through 12 December 2019

ER -

Pham C, Pham V, Dang T. Solar Flare Prediction Using Two-tier Ensemble with Deep Learning and Gradient Boosting Machine. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5844-5853. 9006542. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). https://doi.org/10.1109/BigData47090.2019.9006542