Machine learning techniques can be used in diagnosis of breast cancer to help pathologists and physicians for decision making process. Kernel density estimation is a popular non-parametric method which can be applied for the estimation of data in many diverse applications. Selection of bandwidth and feature subset in kernel density estimator significantly influences the classification performance. In this paper, a PSO-KDE model is proposed that hybridize the particle swarm optimization (PSO) and non-parametric kernel density estimation (KDE) based classifier to diagnosis of breast cancer. In the proposed model, particle swarm optimization is used to simultaneously determine the kernel bandwidth and select the feature subset in the kernel density estimation based classifier. Classification performance and the number of selected features are the criteria used to design the objective function of PSO-KDE. The performance of the PSO-KDE is examined on Wisconsin Breast Cancer Dataset (WBCD) and Wisconsin Diagnosis Breast Cancer Database (WDBC) using classification accuracy, sensitivity and specificity. Experimental results demonstrate that the proposed model has better average performance than GA-KDE model in diagnosis of breast cancer. 
Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer