Characterization and detection of noise in clustering
Pattern Recognition Letters
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Self-Organizing Maps
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
Active semi-supervised fuzzy clustering for image database categorization
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active learning for object classification: from exploration to exploitation
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Classifying large datasets without any a-priori information poses a problem in many tasks. Especially in the field of bioinformatics, often huge unlabeled datasets have to be explored mostly manually by a biology expert. In this work we consider an application that is motivated by the development of high-throughput microscope screening cameras. These devices are able to produce hundreds of thousands of images per day. We propose a new adaptive active classification scheme which establishes ties between the two opposing concepts of unsupervised clustering of the underlying data and the supervised task of classification. Based on Fuzzy c-means clustering and Learning Vector Quantization, the scheme allows for an initial clustering of large datasets and subsequently for the adjustment of the classification based on a small number of carefully chosen examples. Motivated by the concept of active learning, the learner tries to query the most informative examples in the learning process and therefore keeps the costs for supervision at a low level. We compare our approach to Learning Vector Quantization with random selection and Support Vector Machines with Active Learning on several datasets.