Active Semi-Supervised Classification Based on Multiple Clustering Hierarchies
| Autor: | Antonio J.L. Batista, Ricardo J. G. B. Campello, Joerg Sander |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Hierarchy
Computer science business.industry 02 engineering and technology Machine learning computer.software_genre Class (biology) Electronic mail ComputingMethodologies_PATTERNRECOGNITION CURE data clustering algorithm 020204 information systems 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Algorithm design Sensitivity (control systems) Artificial intelligence Cluster analysis business computer |
| Zdroj: | DSAA |
| DOI: | 10.1109/dsaa.2016.9 |
| Popis: | Active semi-supervised learning can play an important role in classification scenarios in which labeled data are difficult to obtain, while unlabeled data can be easily acquired. This paper focuses on an active semi-supervised algorithm that can be driven by multiple clustering hierarchies. If there is one or more hierarchies that can reasonably align clusters with class labels, then a few queries are needed to label with high quality all the unlabeled data. We take as a starting point the well-known Hierarchical Sampling (HS) algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|