The nature of statistical learning theory
The nature of statistical learning theory
Boosting a weak learning algorithm by majority
Information and Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Efficient computations for large least square support vector machine classifiers
Pattern Recognition Letters
A reduced and comprehensible polynomial neural network for classification
Pattern Recognition Letters
Editorial: Hybrid learning machines
Neurocomputing
Logic-oriented neural networks for fuzzy neurocomputing
Neurocomputing
Information Sciences: an International Journal
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Boosting support vector machines for imbalanced data sets
Knowledge and Information Systems
Improving SVM classification on imbalanced time series data sets with ghost points
Knowledge and Information Systems
Improvement of neural network classifier using floating centroids
Knowledge and Information Systems
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
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Breast cancer is one of the most common tumors related to death in women in many countries. In this paper, a novel neural network classification model is developed. The proposed model uses floating centroids method and particle swarm optimization algorithm with inertia weight as optimizer to improve the performance of neural network classifier. Wisconsin breast cancer datasets in UCI Machine Learning Repository are tested with neural network classifier of the proposed method. Experimental results show that the developed model improves search convergence and performance. The accuracy of classification of benign and malignant tumors could be improved by the developed method compared with other classification techniques.