Aiding neural network based image classification with fuzzy-rough feature selection

This paper presents a methodological approach for developing image classifiers that work by exploiting the technical potential of both fuzzy-rough feature selection and neural network-based classification. The use of fuzzy-rough feature selection allows the induction of low-dimensionality feature sets from sample descriptions of real-valued feature patterns of a (typically much) higher dimensionality. The employment of a neural network trained using the induced subset of features ensures the runtime classification performance. The reduction of feature sets reduces the sensitivity of such a neural network-based classifier to its structural complexity. It also minimises the impact of feature measurement noise to the classification accuracy. This work is evaluated by applying the approach to classifying real medical cell images, supported with comparative studies.