Biologically inspired cellular automata learning and prediction model for handwritten pattern recognition

In this study, we propose an ensemble learning architecture called “Cellular Automata Learning and Prediction” (CALP) model, for classification of handwritten patterns. We further propose that every handwritten pattern is an array of living cells or organisms that both interact and are affected by one another. Since the cells impact one another, and have the ability to die and reproduce, we extend this analogy to growth and evolution. Thus every pattern can grow and evolve. We use cellular automata (CA) to model this behavior as it has been used as a default model for various biological systems. Proposed architecture allows the handwritten patterns to evolve or grow using various parameters that control how the cells interact with each other. Then these different evolved patterns are used to train independent classifiers which are then combined together to form an ensemble. The idea is to captures more variations in a handwritten data set than the typical standalone classifiers or their ensembles. The method is applied on 5 handwritten data sets using 5 different classifiers. The experimental results show that our model obtains better classification accuracy on all 5 data sets, even on a small-sized training data. We also compare the performance of CALP with other over-sampling methods.