Hierarchical multi-label prediction of gene function
Bioinformatics
Deep classification in large-scale text hierarchies
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Refined experts: improving classification in large taxonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting domain adaptivity: redo or recycle?
Proceedings of the 20th ACM international conference on Multimedia
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Hierarchical classification (HC) is a popular and efficient way for detecting the semantic concepts from the images. However, the conventional HC, which always selects the branch with the highest classification response to go on, has the risk of propagating serious errors from higher levels of the hierarchy to the lower levels. We argue that the highest-response-first strategy is too arbitrary, because the candidate nodes are considered individually which ignores the semantic relationship among them. In this paper, we propose a novel method for HC, which is able to utilize the semantic relationship among candidate nodes and their children to recover the responses of unreliable classifiers of the candidate nodes, with the hope of providing the branch selection a more globally valid and semantically consistent view. The experimental results show that the proposed method outperforms the conventional HC methods and achieves a satisfactory balance between the accuracy and efficiency.