Farina Faiz
ABSTRACT
Human activities become very difficult to handle when they appear without proper temporal information. A smarter approach is to handle this problem is using multilabel classification. However, most of the multilabel methods are suitable for text data which is very different from activity data. Our research focuses on finding some discriminative approach to make the efficient use of multilabel methods. Our intuition is that a relevance between labels and features is needed to be explored in case of predicting multiple labels, as features are the most important aspects that increase model's performance. In this paper, we proposed a feature selection approach based on feature label relevance method (FLRM) that outperforms an existing feature selection method. We utilized feature-label relevance so that we could find the best fitted features associated with each label.
- Cheng-Xian Li. 2011. Exploiting Label Correlations for Multi-label Classification. (2011).Google Scholar
- Farina Faiz Yoshinori Ideno Hiromichi Iwasaki Yoko Muroi, Sozo Inoue. 2020. Multilabel Classification of Nursing Activities in a Realistic Scenario. In 2nd Activity and Behavior Computing.Google Scholar
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Multilabel classification in human activity recognition: PhD forum abstract
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