International Journal of Systems Science
Expert Systems with Applications: An International Journal
A Combination Classification Algorithm Based on Outlier Detection and C4.5
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Kernel-matching pursuits with arbitrary loss functions
IEEE Transactions on Neural Networks
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Proceedings of the international conference on Multimedia information retrieval
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Weighted extreme learning machine for imbalance learning
Neurocomputing
The design of polynomial function-based neural network predictors for detection of software defects
Information Sciences: an International Journal
A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
A Fast Multiclass Classification Algorithm Based on Cooperative Clustering
Neural Processing Letters
Multimedia Tools and Applications
Imbalanced evolving self-organizing learning
Neurocomputing
| Hi-index | 0.00 |
Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm