Modern heuristic techniques for combinatorial problems
Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Robust Learning with Missing Data
Machine Learning
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised model-based document clustering: A comparative study
Machine Learning
Graph-based Semi-supervised Learning Algorithm for Web Page Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A high-performance semi-supervised learning method for text chunking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Artificial Intelligence in Medicine
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Graph sharpening plus graph integration
Bioinformatics
Breast cancer survivability via AdaBoost algorithms
HDKM '08 Proceedings of the second Australasian workshop on Health data and knowledge management - Volume 80
On Efficient Large Margin Semisupervised Learning: Method and Theory
The Journal of Machine Learning Research
Toward breast cancer survivability prediction models through improving training space
Expert Systems with Applications: An International Journal
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Graph-based semi-supervised learning as a generative model
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Semi-supervised learning of visual classifiers from web images and text
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A graph-based semi-supervised learning for question-answering
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Expert Systems with Applications: An International Journal
wFDT: weighted fuzzy decision trees for prognosis of breast cancer survivability
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Objective: Many machine learning models have aided medical specialists in diagnosis and prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the performance evaluation of the models, but stability which indicates the robustness of the performance to model parameter variation also becomes essential. A stable model is in practice of benefit to the medical specialists who may have little expertise in model tuning. The main purpose of this work is to address the importance of the stability of a model and to suggest one of such models. Methods: A comparative study of three prominent machine learning models was carried out for the prognosis of breast-cancer survivability: support vector machines, artificial neural networks, and semi-supervised learning models. Material: The surveillance, epidemiology, and end results database for breast cancer was used, which is known as the most comprehensive source of information on cancer incidence in the United States. Results: The best performance was obtained from the semi-supervised learning model. It showed good overall accuracy and stability under model parameter variation. The sharpening procedure enhanced the stability of the model via the noise-reduction. Conclusion: We suggest that semi-supervised learning model is a good candidate that medical professionals readily employ without consuming the time and effort for parameter searching for a specific model. The ease of use and faster time to results of the predictive model will eventually lead to the accurate and less-invasive prognosis for breast cancer patients.