A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Active Learning with Local Models
Neural Processing Letters
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Selective sampling for example-based word sense disambiguation
Computational Linguistics
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Combining Diversity-Based Active Learning with Discriminant Analysis in Image Retrieval
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Active relevance feedback for difficult queries
Proceedings of the 17th ACM conference on Information and knowledge management
Active learning for object classification: from exploration to exploitation
Data Mining and Knowledge Discovery
A Density-Based Re-ranking Technique for Active Learning for Data Annotations
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
Collaborative case retention strategies for CBR agents
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Index driven selective sampling for CBR
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Incorporating diversity and density in active learning for relevance feedback
ECIR'07 Proceedings of the 29th European conference on IR research
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The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present a simple but effective exploration-only selection strategy for AL in the textual domain. Our approach is inherently case-based, using only nearest-neighbour-based density and diversity measures. We show how its performance is comparable to the more computationally expensive exploitation-based approaches and that it offers the opportunity to be classifier independent.