Overlap-Based Similarity Metrics for Motif Search in DNA Sequences
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Genetic algorithms as a pre processing strategy for imbalanced datasets
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Towards improving automatic image annotation using improvised fractal SMOTE approach
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Expert Systems with Applications: An International Journal
Predicting Protein-Ligand Binding Site Using Support Vector Machine with Protein Properties
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, and implicitly assume that all misclassification errors cost equally. But data in real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining community. The tradition machine learning algorithms have bad performance when they learn from imbalanced data sets. Thus, machine learning on imbalanced data sets becomes an urgent problem. The importance of imbalanced data sets and their broad application domains in data mining are introduced, and then methods to deal with the class imbalance problem are discussed and their effectiveness are compared. Last but not least, the existing evaluation measures of class imbalance problem are systematically analyzed.