An Innovative Algorithm for Feature Selecton Based on Rough Set with Fuzzy C-means Clustering

Feature selection is a fundamental problem in data mining, especially for high level dimensional datasets. Feature selection is a process commonly used in machine learning, wherein subsets of the features from the original set of features are selected for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number of input features thus to identify the key features of breast cancer diagnosis using fuzzy c-means clustering (FCM), K-nearest neighbors (KNN) and rough set. The results show that the hybrid method is able to produce more accurate diagnosis and prognosis results than the full input model with respect to computational complexity and classification accuracy.