Optimizing feature-aset in image classification using a hybrid approach

Classification is categorizing different input dataset to respective class labels. Classification has been an important area of research. Image classification deals with the categorization of images. There are many problems those needed to be addressed in this area. Feature subset selection is a significant stage for improving the complexity. A hybrid method / approach constituting of feature subset selection and classification is used for obtaining results. Feature subset selection (FSS) is attained by performing GA and classification is performed by neural networks. Cervical cancer behavior risk dataset is a standard dataset taken from UCI repository. Feature subset selection is performed to obtain an optimized feature set. Classification is implemented on this reduced feature set using neural network.