Granular neural networks: A study of optimizing allocation of information granularity in input space.

Abstract In this paper, we develop a granular input space for neural networks, especially for multilayer perceptrons (MLPs). Unlike conventional neural networks, a neural network with granular input is an augmented study on a basis of a well learned numeric neural network. We explore an efficient way of forming granular input variables so that the corresponding granular outputs of the neural network achieve the highest values of the criteria of specificity (and support). When we augment neural networks through distributing information granularities across input variables, the output of a network has different levels of sensitivity on different input variables. Capturing the relationship between input variables and output result becomes of a great help for mining knowledge from the data. And in this way, important features of the data can be easily found. As an essential design asset, information granules are considered in this construct. The quantification of information granules is viewed as levels of granularity which is given by the expert. The detailed optimization procedure of allocation of information granularity is realized by an improved partheno genetic algorithm (IPGA). The proposed algorithm is testified effective by some numeric studies completed for synthetic data and data coming from the machine learning and StatLib repositories. Moreover, the experimental studies offer a deep insight into the specificity of input features. Highlights • An algorithm of developing a granular neural network with granular input on basis of a designed network is proposed. • The influence of different levels of information granularity on the performance of the granular network is studied. • An improved partheno genetic algorithm is used to optimize the allocation of information granularity. • Synthetic data and real world data sets are used to testify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]