Improving imbalanced students’ text feedback classification using re-sampling based approach

Zainab Mutlaq Ibrahim; Mohamed Bader-El-Den; Mihaela Cocea

doi:10.1007/978-3-030-29933-0_22

Improving imbalanced students’ text feedback classification using re-sampling based approach

Zainab Mutlaq Ibrahim
, Mohamed Bader-El-Den
, Mihaela Cocea

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

318 Downloads (Pure)

Abstract

Class imbalance is a major problem in text classification, the problem happens when the used machine learning algorithm biases towards the majority class, so this makes it incorrectly classifies minority class instances. To get over this problem, investigators use the Synthetic Minority Oversampling Technique (SMOTE), it is pre-processing algorithm which was proven as a very good solution for handling imbalanced data sets. In this paper an empirical study have been executed to handle three imbalanced data sets in text format using SMOTE, the recall of all minority classes significantly improved in addition of significant improvement in all models overall performance.

Average classes’ recall was improved significantly, by 0.15, 0.09, 0.10 in classification of ASS, FDS, NASS data sets respectively. While the recall for the minority class has significantly increased, ASS (0.23), FDS (0.08), and NASS (0.15).

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK
Editors	Zhaojie Ju, Longzhi Yang, Chenguang Yang, Alexander Gegov, Dalin Zhou
Publisher	Springer
Pages	262-267
ISBN (Electronic)	978-3-030-29933-0
ISBN (Print)	978-3-030-29932-3
DOIs	https://doi.org/10.1007/978-3-030-29933-0_22
Publication status	Published - Sept 2019
Event	19th UK Workshop on Computational Intelligence - Portsmouth, United Kingdom Duration: 4 Sept 2019 → 5 Sept 2019 Conference number: 19 https://www.ukci2019.port.ac.uk/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	1043
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Workshop

Workshop	19th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2019
Country/Territory	United Kingdom
City	Portsmouth
Period	4/09/19 → 5/09/19
Other	The UKCI 2019 covers both theory and applications in computational intelligence. The topics of interest include Fuzzy Systems Neural Networks Evolutionary Computation Evolving Systems Machine Learning Data Mining Cognitive Computing Intelligent Robotics Hybrid Methods Deep Learning Applications of Computational Intelligence
Internet address	https://www.ukci2019.port.ac.uk/

Access to Document

10.1007/978-3-030-29933-0_22

Improving imbalanced students
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-29933-0_22.
Accepted author manuscript (Post-print), 171 KB

Cite this

Mutlaq Ibrahim, Z., Bader-El-Den, M., & Cocea, M. (2019). Improving imbalanced students’ text feedback classification using re-sampling based approach. In Z. Ju, L. Yang, C. Yang, A. Gegov, & D. Zhou (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK (pp. 262-267). (Advances in Intelligent Systems and Computing; Vol. 1043). Springer. https://doi.org/10.1007/978-3-030-29933-0_22

Mutlaq Ibrahim, Zainab ; Bader-El-Den, Mohamed ; Cocea, Mihaela. / Improving imbalanced students’ text feedback classification using re-sampling based approach. Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK. editor / Zhaojie Ju ; Longzhi Yang ; Chenguang Yang ; Alexander Gegov ; Dalin Zhou. Springer, 2019. pp. 262-267 (Advances in Intelligent Systems and Computing).

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title = "Improving imbalanced students{\textquoteright} text feedback classification using re-sampling based approach",

abstract = "Class imbalance is a major problem in text classification, the problem happens when the used machine learning algorithm biases towards the majority class, so this makes it incorrectly classifies minority class instances. To get over this problem, investigators use the Synthetic Minority Oversampling Technique (SMOTE), it is pre-processing algorithm which was proven as a very good solution for handling imbalanced data sets. In this paper an empirical study have been executed to handle three imbalanced data sets in text format using SMOTE, the recall of all minority classes significantly improved in addition of significant improvement in all models overall performance. Average classes{\textquoteright} recall was improved significantly, by 0.15, 0.09, 0.10 in classification of ASS, FDS, NASS data sets respectively. While the recall for the minority class has significantly increased, ASS (0.23), FDS (0.08), and NASS (0.15).",

author = "\{Mutlaq Ibrahim\}, Zainab and Mohamed Bader-El-Den and Mihaela Cocea",

year = "2019",

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doi = "10.1007/978-3-030-29933-0\_22",

language = "English",

isbn = "978-3-030-29932-3",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "262--267",

editor = "Ju, \{Zhaojie \} and Yang, \{Longzhi \} and Chenguang Yang and Alexander Gegov and Dalin Zhou",

booktitle = "Advances in Computational Intelligence Systems",

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note = "19th UK Workshop on Computational Intelligence, UKCI 2019 ; Conference date: 04-09-2019 Through 05-09-2019",

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Mutlaq Ibrahim, Z, Bader-El-Den, M & Cocea, M 2019, Improving imbalanced students’ text feedback classification using re-sampling based approach. in Z Ju, L Yang, C Yang, A Gegov & D Zhou (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK. Advances in Intelligent Systems and Computing, vol. 1043, Springer, pp. 262-267, 19th UK Workshop on Computational Intelligence, Portsmouth, United Kingdom, 4/09/19. https://doi.org/10.1007/978-3-030-29933-0_22

Improving imbalanced students’ text feedback classification using re-sampling based approach. / Mutlaq Ibrahim, Zainab; Bader-El-Den, Mohamed ; Cocea, Mihaela.
Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK. ed. / Zhaojie Ju; Longzhi Yang; Chenguang Yang; Alexander Gegov; Dalin Zhou. Springer, 2019. p. 262-267 (Advances in Intelligent Systems and Computing; Vol. 1043).

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

TY - GEN

T1 - Improving imbalanced students’ text feedback classification using re-sampling based approach

AU - Mutlaq Ibrahim, Zainab

AU - Bader-El-Den, Mohamed

AU - Cocea, Mihaela

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PY - 2019/9

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N2 - Class imbalance is a major problem in text classification, the problem happens when the used machine learning algorithm biases towards the majority class, so this makes it incorrectly classifies minority class instances. To get over this problem, investigators use the Synthetic Minority Oversampling Technique (SMOTE), it is pre-processing algorithm which was proven as a very good solution for handling imbalanced data sets. In this paper an empirical study have been executed to handle three imbalanced data sets in text format using SMOTE, the recall of all minority classes significantly improved in addition of significant improvement in all models overall performance. Average classes’ recall was improved significantly, by 0.15, 0.09, 0.10 in classification of ASS, FDS, NASS data sets respectively. While the recall for the minority class has significantly increased, ASS (0.23), FDS (0.08), and NASS (0.15).

AB - Class imbalance is a major problem in text classification, the problem happens when the used machine learning algorithm biases towards the majority class, so this makes it incorrectly classifies minority class instances. To get over this problem, investigators use the Synthetic Minority Oversampling Technique (SMOTE), it is pre-processing algorithm which was proven as a very good solution for handling imbalanced data sets. In this paper an empirical study have been executed to handle three imbalanced data sets in text format using SMOTE, the recall of all minority classes significantly improved in addition of significant improvement in all models overall performance. Average classes’ recall was improved significantly, by 0.15, 0.09, 0.10 in classification of ASS, FDS, NASS data sets respectively. While the recall for the minority class has significantly increased, ASS (0.23), FDS (0.08), and NASS (0.15).

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SN - 978-3-030-29932-3

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BT - Advances in Computational Intelligence Systems

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T2 - 19th UK Workshop on Computational Intelligence

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Mutlaq Ibrahim Z, Bader-El-Den M , Cocea M. Improving imbalanced students’ text feedback classification using re-sampling based approach. In Ju Z, Yang L, Yang C, Gegov A, Zhou D, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK. Springer. 2019. p. 262-267. (Advances in Intelligent Systems and Computing). Epub 2019 Aug 30. doi: 10.1007/978-3-030-29933-0_22

Improving imbalanced students’ text feedback classification using re-sampling based approach

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