Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Swarm Intelligence in Data Mining (Studies in Computational Intelligence)
Flexible neural trees ensemble for stock index modeling
Neurocomputing
ICA Based on KPCA and Hybrid Flexible Neural Tree to Face Recognition
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Probabilistic incremental program evolution
Evolutionary Computation
Modeling of Cement Decomposing Furnace Production Process Based on Flexible Neural Tree
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Pattern Recognition Using Neural and Functional Networks
Pattern Recognition Using Neural and Functional Networks
Intelligent visual recognition and classification of cork tiles with neural networks
IEEE Transactions on Neural Networks
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Robust and accurate cancer diagnosis and classification is very important in cancer treatment. A microarray data produce a large amount of data that are irrelevant, noisy and highly dimensional. Most of the genes are uninformative which degrades the performance of data mining and machine learning tasks. To reduce the curse of dimensionality, a preprocessing step known as feature selection is done. Feature selection is referred as selecting only a fraction of features that are most predictive of a given outcome. To deal with these issues, classification tools should robustly learn to identify a subset of informative genes embedded in large data set that has high dimensional noises. In this paper, an integrated approach of FNT (Flexible Neural Tree) and swarm optimization is proposed to simultaneously optimize the selection of feature subset and the classifier. A hierarchical neural network like structure is flexible neural tree (FNT).which is automatically created and optimized using evolutionary like algorithms to solve a given problem. Because of the most distinctive feature of flexible neural tree structure, it is not necessary to set the structure and weights of neural networks prior the problem is solved. The architecture of FNT is created with Ant Colony Optimization (ACO) and the parameters of the neural tree are optimized by Particle Swarm Optimization (PSO) algorithm and its enhancement (EPSO). The experimental results indicate that the proposed technique is feasible and efficient for the classification of microarray data.