Swarm intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Information Retrieval
Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Concept-Learning in the Presence of Between-Class and Within-Class Imbalances
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Does cost-sensitive learning beat sampling for classifying rare classes?
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Data mining for improved cardiac care
ACM SIGKDD Explorations Newsletter
The class imbalance problem: A systematic study
Intelligent Data Analysis
Fast learning in networks of locally-tuned processing units
Neural Computation
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
International Journal of Systems Science
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Boosting support vector machines for imbalanced data sets
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
International Journal of Bio-Inspired Computation
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experiments with repeating weighted boosting search for optimization signal processing applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A robust nonlinear identification algorithm using PRESS statistic and forward regression
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Kernel-Based Two-Class Classifier for Imbalanced Data Sets
IEEE Transactions on Neural Networks
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
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
Boosting weighted ELM for imbalanced learning
Neurocomputing
| Hi-index | 0.01 |
This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.