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 proceedingConference contribution

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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 languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK
EditorsZhaojie Ju, Longzhi Yang, Chenguang Yang, Alexander Gegov, Dalin Zhou
ISBN (Electronic)978-3-030-29933-0
ISBN (Print)978-3-030-29932-3
Publication statusPublished - Sept 2019
Event19th UK Workshop on Computational Intelligence - Portsmouth, United Kingdom
Duration: 4 Sept 20195 Sept 2019
Conference number: 19

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Workshop19th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2019
Country/TerritoryUnited Kingdom
OtherThe 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


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