Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The class imbalance problem in learning classifier systems: a preliminary study
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
The class imbalance problem: A systematic study
Intelligent Data Analysis
A New Approach to Fuzzy-Rough Nearest Neighbour Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Selective sampling methods in one-class classification problems
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
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Breast tissue characteristics are widely accepted as important indicators of the likelihood of the developing breast cancer Methods which have the ability to automatically classify breast tissue distribution therefore provide important tools in assessing the risk to which patients are exposed This paper examines the machine learning techniques employed for knowledge discovery in a recent approach to mammographic risk assessment A number of weaknesses for selected classification techniques are identified and examined Additionally, important trends in the data such as decision class confusion and how this affects the ability to perform accurate knowledge discovery on the extracted image data are also explored The paper is concluded with some ideas as to how the identified trends in the data and weaknesses in the classification approaches could be addressed.