Author Identifier
Suku Sukunesan: https://orcid.org/0000-0002-8563-3469
Document Type
Journal Article
Publication Title
Artificial Intelligence Review
Volume
58
Issue
8
Publisher
Springer
School
School of Business and Law
Funders
Medical Research Future Fund (MRFF APP1179321) / Researchers Supporting Project (RSPD2025R681) King Saud University, Riyadh, Saudi Arabia
Abstract
Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarkable challenges for ED classification. To overcome these hurdles, we introduce a novel method, an informed branch and bound search technique known as ED-Filter. This strategy significantly improves the drawbacks of conventional feature selection algorithms such as filters and wrappers. ED-Filter iteratively identifies an optimal set of promising features that maximize the eating disorder classification accuracy. In order to adapt to the dynamic nature of Twitter ED data, we enhance the ED-Filter with a hybrid greedy-based deep learning algorithm. This algorithm swiftly identifies sub-optimal features to accommodate the ever-evolving data landscape. Experimental results on Twitter eating disorder data affirm the effectiveness and efficiency of ED-Filter. The method demonstrates significant improvements in classification accuracy and proves its value in eating disorder detection on social media platforms.
DOI
10.1007/s10462-025-11244-4
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Naseriparsa, M., Sukunesan, S., Cai, Z., Alfarraj, O., Tolba, A., Fathi Rabooki, S., & Xia, F. (2025). ED-Filter: Dynamic feature filtering for eating disorder classification. Artificial Intelligence Review, 58. https://doi.org/10.1007/s10462-025-11244-4