# ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification

arXiv: https://arxiv.org/abs/2501.14785 · HTML: https://mlsanity.com/paper/2501.14785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 73 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 10 of 10)

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- [Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining](https://mlsanity.com/paper/2008.07097/) (2020)
- [Data-driven Computational Social Science: A Survey](https://mlsanity.com/paper/2008.12372/) (2020)
- [Toward Optimal Feature Selection in Naive Bayes for Text Categorization](https://mlsanity.com/paper/1602.02850/) (2016)
- [Deep Outdated Fact Detection in Knowledge Graphs](https://mlsanity.com/paper/2402.03732/) (2024)
- [Mobile Multimedia Recommendation in Smart Communities: A Survey](https://mlsanity.com/paper/1312.6565/) (2013)
- [Discovering Structure in High-Dimensional Data Through Correlation Explanation](https://mlsanity.com/paper/1406.1222/) (2014)
- [No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results](https://mlsanity.com/paper/1703.02212/) (2017)
- [Analyzing Social Media Data to Understand Consumers' Information Needs on Dietary Supplements](https://mlsanity.com/paper/1906.03171/) (2019)
- [Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts](https://mlsanity.com/paper/2402.05536/) (2024)

## Abstract (arXiv)

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.