The nature of statistical learning theory
The nature of statistical learning theory
Introduction to support vector learning
Advances in kernel methods
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning adaptive kernels for model diagnosis
Design and application of hybrid intelligent systems
On Text-based Mining with Active Learning and Background Knowledge Using SVM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Clustering-Based Approach to Predict Outcome in Cancer Patients
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Improving Text Classification Performance with Incremental Background Knowledge
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Cancer is becoming a human plague, and decision-support tools to help physicians better diagnosing are a fulsome research field. False negatives can be a huge problem for cancer diagnosticians, since while a false positive can result in time and money lost, a false negative can result in the lost of human lives, which puts an overwhelming burden on diagnosis. In this framework, we propose a two-fold approach to purge false negatives in cancer diagnosis without compromising precision performance. First, we use an incremental background knowledge method and then, an active learning strategy completes the procedure. The defined incremental active learning SVM method was tested in the Wisconsin-Madison breast cancer diagnosis problem showing the effectiveness of such techniques in supporting cancer diagnosis.