SMOTE-least square support vector machine for classification of multiclass imbalanced data

Santi Wulan Purnami; Rani Kemala Trapsilasiwi

doi:10.1145/3055635.3056581

SMOTE-least square support vector machine for classification of multiclass imbalanced data

Santi Wulan Purnami
, Rani Kemala Trapsilasiwi

Institut Teknologi Sepuluh Nopember

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

11 Citations (Scopus)

Abstract

Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.

Original language	English
Title of host publication	Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
Publisher	Association for Computing Machinery
Pages	107-111
Number of pages	5
ISBN (Electronic)	9781450348171
DOIs	https://doi.org/10.1145/3055635.3056581
Publication status	Published - 24 Feb 2017
Event	9th International Conference on Machine Learning and Computing, ICMLC 2017 - Singapore, Singapore Duration: 24 Feb 2017 → 26 Feb 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128357

Conference

Conference	9th International Conference on Machine Learning and Computing, ICMLC 2017
Country/Territory	Singapore
City	Singapore
Period	24/02/17 → 26/02/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Imbalanced data
Least square support vector machine
Multiclass
Particle swarm optimization-gravitational search algorithm
Synthetic minority oversampling technique

Access to Document

10.1145/3055635.3056581

Cite this

Purnami, S. W., & Trapsilasiwi, R. K. (2017). SMOTE-least square support vector machine for classification of multiclass imbalanced data. In Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017 (pp. 107-111). (ACM International Conference Proceeding Series; Vol. Part F128357). Association for Computing Machinery. https://doi.org/10.1145/3055635.3056581

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abstract = "Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.",

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Purnami, SW & Trapsilasiwi, RK 2017, SMOTE-least square support vector machine for classification of multiclass imbalanced data. in Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017. ACM International Conference Proceeding Series, vol. Part F128357, Association for Computing Machinery, pp. 107-111, 9th International Conference on Machine Learning and Computing, ICMLC 2017, Singapore, Singapore, 24/02/17. https://doi.org/10.1145/3055635.3056581

SMOTE-least square support vector machine for classification of multiclass imbalanced data. / Purnami, Santi Wulan; Trapsilasiwi, Rani Kemala.
Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017. Association for Computing Machinery, 2017. p. 107-111 (ACM International Conference Proceeding Series; Vol. Part F128357).

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

TY - GEN

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AU - Trapsilasiwi, Rani Kemala

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N2 - Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.

AB - Dealing with multiclass classification problem is still considered as significant hurdle to determine an efficient classifier. Moreover, this task is getting rough when it comes to imbalanced data, which defined as the number of some classes are much bigger than the others. This condition could cause the classifier tends to predict the majority class and ignore the minority class. This study proposed Synthetic Minority Oversampling Technique-Least Square Support Vector Machine (SMOTE-LSSVM) to build a classifier addressing this problem. Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) was used to optimize the parameters of LS-SVM, while SMOTE was employed to balance the data. The effectiveness of SMOTE-LSSVM was examined on malignancy of breast cancer dataset. Results of this studies showed that the accuracy rate after applying SMOTE increased significantly compare to the results without applying SMOTE.

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KW - Least square support vector machine

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KW - Particle swarm optimization-gravitational search algorithm

KW - Synthetic minority oversampling technique

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ER -

SMOTE-least square support vector machine for classification of multiclass imbalanced data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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