C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
A Bootstrap Technique for Nearest Neighbor Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Information Processing Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Application of elitist multi-objective genetic algorithm for classification rule generation
Applied Soft Computing
An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks
Neural Processing Letters
Particle Swarm Optimization of Neural Network Architectures andWeights
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Information Sciences: an International Journal
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
A modal learning adaptive function neural network applied to handwritten digit recognition
Information Sciences: an International Journal
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Neural Computing and Applications - Special Issue - KES2008
Evolutionarily optimized features in functional link neural network for classification
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Training neural networks using multiobjective particle swarm optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Modeling meteorological prediction using particle swarm optimization and neural network ensemble
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Particle Swarm Optimization to Design Ideotypes for Sustainable Fruit Production Systems
International Journal of Swarm Intelligence Research
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Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN=ISO-FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).