Breast Cancer Diagnosis Using K-Means Type-2 Fuzzy Neural Network

This paper aims to design a classifier using the K-means clustering algorithm and the interval type-2 fuzzy neural network (IT2FNN). Firstly, the K-means clustering algorithm will classify the training data into k groups, according to its characteristics. After that, the IT2FNN will train the k classifiers' structure with these data. The testing data will be also determined that they will belong to which classifier. With this parallel structure, the performance of the proposed classifier is competitive with some state-of-the-art techniques. The parameter adaptive laws of the network are derived by using the steepest descent gradient approach. The convergence and stability of the proposed algorithm is guaranteed using the Lyapunov function. The system performance is evaluated by the breast cancer datasets of the University of California at Irvine (UCI). Comparison with other classifiers is also conducted. The experimental results have shown the effectiveness of the proposed method.