Abstract
Multi-label classification refers to the problem that predicts each single instance to be one or more labels in a set of associated labels. It is common in many real-world applications such as text categorization, functional genomics and semantic scene classification. The main challenge for multi-label classification is predicting the labels of a new instance with the exponential number of possible label sets. Previous works mainly pay attention to transforming the multi-label classification to be single-label classification or modifying the existing traditional algorithm. In this paper, a novel algorithm which combines the advantage of the famous KNN and Random Walk algorithm (RW.KNN) is proposed. The KNN based link graph is built with the k-nearest neighbors for each instance. For an unseen instance, a random walk is performed in the link graph. The final probability is computed according to the random walk results. Lastly, a novel algorithm based on minimizing Hamming Loss to select the classification threshold is also proposed in this paper.
Original language | English |
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Title of host publication | CIKM 2011 Glasgow |
Subtitle of host publication | PIKM'11 - Proceedings of the 2011 Workshop for Ph.D. Students in Information and Knowledge Management |
Publisher | Association for Computing Machinery (ACM) |
Pages | 87-90 |
Number of pages | 4 |
ISBN (Print) | 9781450309530 |
DOIs | |
Publication status | Published - 15 Dec 2011 |
Externally published | Yes |
Event | Workshop for Ph.D. Students in Information and Knowledge Management, PIKM'11 - Glasgow, United Kingdom Duration: 28 Oct 2011 → 28 Oct 2011 Conference number: 4th |
Publication series
Name | International Conference on Information and Knowledge Management, Proceedings |
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Conference
Conference | Workshop for Ph.D. Students in Information and Knowledge Management, PIKM'11 |
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Country/Territory | United Kingdom |
City | Glasgow |
Period | 28/10/11 → 28/10/11 |
Keywords
- knn
- link graph
- multi-label classification
- random walk