Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Between Classification-Error Approximation and Weighted Least-Squares Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic neural classification
Neural Computation
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Incremental learning of new classes in unbalanced datasets: Learn++.UDNC
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Incremental Learning of Chunk Data for Online Pattern Classification Systems
IEEE Transactions on Neural Networks
Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
An online learning network for biometric scores fusion
Neurocomputing
| Hi-index | 0.00 |
Most of the existing sequential learning methods for class imbalance learn data in chunks. In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). WOS-ELM is a general online learning method that alleviates the class imbalance problem in both chunk-by-chunk and one-by-one learning. One of the new features of WOS-ELM is that an appropriate weight setting for CIL is selected in a computationally efficient manner. In one-by-one learning of WOS-ELM, a new sample can update the classification model without waiting for a chunk to be completed. Extensive empirical evaluations on 15 imbalanced datasets show that WOS-ELM obtains comparable or better classification performance than competing methods. The computational time of WOS-ELM is also found to be lower than that of the competing CIL methods.