A hierarchical GMDH-based polynomial neural network for handwritten numeral recognition using topological features

We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving the classification of handwritten numerals. It also has the advantage of removing the need to resolve classification ties that exist in other forms of combining a number of classifiers to solve a multiclass classification problem whether using one-versus-all or one-versus-one approaches. The proposed approach is empirically evaluated and compared with five other state-of-the-art machine learning classifiers using a publicly available dataset based on non-Gaussian topological features.