用动态筛选方法提升社交媒体饮食障碍识别准确率
ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification
- 基于分支定界法迭代筛选最优特征集
- 在推特数据上分类准确率显著提升
- 适合关注心理健康与社交媒体分析的研究者
饮食障碍（ED）是严重的精神健康问题，日益受到心理卫生领域的关注。心理健康专业人士越来越重视来自推特等社交媒体平台的数据价值。然而，推特数据的高维性和海量特征给饮食障碍分类带来巨大挑战。为此，我们提出一种新型方法——ED-Filter，即基于知识引导的分支定界搜索技术，显著克服了传统特征选择算法（如过滤器和包装器）的缺陷。ED-Filter通过迭代识别一组能最大化分类准确率的有前景特征。为适应推特饮食障碍数据的动态特性，我们进一步结合贪心策略与深度学习算法，快速识别次优特征以应对不断变化的数据环境。在推特饮食障碍数据上的实验结果验证了ED-Filter的有效性与高效性，其分类准确率显著提升，证明了该方法在社交媒体饮食障碍检测中的实用价值。
原文摘要 · Abstract (English)
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.
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