Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
How to help seismic analysts to verify the French seismic bulletin?
Engineering Applications of Artificial Intelligence
Adaptive prototype-based fuzzy classification
Fuzzy Sets and Systems
Active learning for object classification: from exploration to exploitation
Data Mining and Knowledge Discovery
Effective multi-label active learning for text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Adaptive active classification of cell assay images
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Remote sensing image segmentation by active queries
Pattern Recognition
Activized learning: transforming passive to active with improved label complexity
The Journal of Machine Learning Research
Active learning with multi-label SVM classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents an active learning method which reduces the labeling effort of domain experts in multi-class classification problems. Active learning is applied in conjunction with support vector machines to recognize underwater zooplankton from higher-resolution, new generation SIPPER II images. Most previous work on active learning with support vector machines only deals with two class problems. In this paper, we propose an active learning approach "breaking ties" for multi-class support vector machines using the one-vs-one approach with a probability approximation. Experimental results indicate that our approach often requires significantly less labeled images to reach a given accuracy than the approach of labeling the least certain test example and random sampling. It can also be applied in batch mode resulting in an accuracy comparable to labeling one image at a time and retraining.