Information-based objective functions for active data selection
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Active Learning with Local Models
Neural Processing Letters
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Bionformatics Computing
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Case-based reasoning as a decision support system for cancer diagnosis: A case study
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
CBR System with Reinforce in the Revision Phase for the Classification of CLL Leukemia
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
MicroCBR: A case-based reasoning architecture for the classification of microarray data
Applied Soft Computing
Active learning for protein function prediction in protein-protein interaction networks
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
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In supervised learning it is assumed that it is straightforward to obtain labeled data. However, in reality labeled data can be scarce or expensive to obtain. Active learning (AL) is a way to deal with the above problem by asking for the labels of the most ''informative'' data points. We propose an AL method based on a metric of classification confidence computed on a feature subset of the original feature space which pertains especially to the large number of dimensions (i.e. examined genes) of microarray experiments. DNA microarray expression experiments permit the systematic study of the correlation of the expression of thousands of genes. Feature selection is critical in the algorithm because it enables faster and more robust retraining of the classifier. The approach that is followed for feature selection is a combination of a variance measure and a genetic algorithm. We have applied the proposed method on DNA microarray data sets with encouraging results. In particular we studied data sets concerning: small round blue cell tumours (4 types), Leukemia (2 types), lung cancer (2 types) and prostate cancer (healthy, unhealthy)