Linear models for unbalanced data
Linear models for unbalanced data
Classification by feature partitioning
Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Feature selection on hierarchy of web documents
Decision Support Systems - Web retrieval and mining
Improving Performance of Parallel Transaction Processing Systems by Balancing Data Load on Line
ICPADS '00 Proceedings of the Seventh International Conference on Parallel and Distributed Systems
A neural network model with bounded-weights for pattern classification
Computers and Operations Research
Learning Rules from Highly Unbalanced Data Sets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Artificial Intelligence in Medicine
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Bio-inspired fuzzy logic based tuning of power system stabilizer
Expert Systems with Applications: An International Journal
Rule-based machine learning methods for functional prediction
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
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
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Unbalanced data that are minority classes with few samples presented in many fields. The mean of unbalanced data is difficult to formalize so that traditional algorithms are limited in solving unbalanced data. In this paper, a novel algorithm based on analysis of variance (ANOVA), fuzzy C-means (FCM) and bacterial foraging optimization (BFO) is proposed to classify unbalanced data. ANOVA can measure the difference between the means of two or more groups in which the observed variance is partitioned into components due to various explanatory variables. FCM is a method of fuzzy clustering algorithm that allows one piece of data to belong to two or more clusters. Natural selection tends to eliminate animals with poor foraging strategies and favors the propagation of genes of those animals that have successful foraging strategies. BFO can model the mechanism of natural selection and solve many application problems. The proposed algorithm combines the advantages of ANOVA, FCM and BFO. ANOVA has the ability to select beneficial feature subsets. FCM has the ability to identify data into clusters with certain membership degrees, and BFO has the fast ability to converge to global optima. In this paper, microarray data of ovarian cancer and zoo dataset are used to test the performance for the proposed algorithm. The performance of the proposed algorithm is supported by simulation results. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing approaches.