Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Multiresolution mammogram analysis in multilevel decomposition
Pattern Recognition Letters
Fuzzy rough sets hybrid scheme for breast cancer detection
Image and Vision Computing
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
Computers in Biology and Medicine
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Artificial Intelligence in Medicine
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Computer-aided evaluation of screening mammograms based on local texture models
IEEE Transactions on Image Processing
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
A 'non-parametric' version of the naive Bayes classifier
Knowledge-Based Systems
A filter-based approach towards automatic detection of microcalcification
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Severe class imbalance: why better algorithms aren't the answer
ECML'05 Proceedings of the 16th European conference on Machine Learning
IEEE Transactions on Information Technology in Biomedicine
A Kernel-Based Two-Class Classifier for Imbalanced Data Sets
IEEE Transactions on Neural Networks
Automatic microcalcification and cluster detection for digital and digitised mammograms
Knowledge-Based Systems
Credit risk assessment and decision making by a fusion approach
Knowledge-Based Systems
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
On-line fast palmprint identification based on adaptive lifting wavelet scheme
Knowledge-Based Systems
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
Automated identification of normal and diabetes heart rate signals using nonlinear measures
Computers in Biology and Medicine
Data driven process modeling and simulation: an applied case study
Proceedings of the 2013 Summer Computer Simulation Conference
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Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques. Although some work suggest that SVM performs better than ANN, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve Az. This performance may become much worse when the training samples are imbalanced. As a result, a new strategy namely balanced learning with optimized decision making is proposed to enable effective learning from imbalanced samples, which is further employed to evaluate the performance of ANN and SVM in this context. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from both ANN and SVM has been significantly improved. Although ANN outperforms SVM when balanced learning is absent, the performance from the two classifiers becomes very comparable when both balanced learning and optimized decision making are employed. Consequently, an average improvement of more than 10% in the measurements of F"1 score and Az measurement are achieved for the two classifiers. This has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.