Blood sugar regularization based evolutionary algorithm for data classification

No one can fool mother nature but we can learn from her, device many new methodologies through bio mimicry, since nature is the single and most complex system that has been field tested the longest. Being inspired by the mechanism through which our mother nature handling our blood sugar level, in this paper we proposed a new evolutionary algorithm for classification based on it. In this process we have identified that feature selection plays a vital role in deciding the performance behaviour of classifiers and an efficient feature or attribute selection can considerably augment the classification accuracy as well as reduces the run time of the algorithm. The paper describes the philosophy of optimum blood sugar controlling strategy being implemented in optimizing the feature selection and precision process of the classifier in the form of an algorithm. The efficiency of proposed algorithm is demonstrated experimentally on classifying the Iris dataset and Wine recognition dataset together with our laboratory generated humanoid robot dataset.

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