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Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localiza-tion of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellu-lar localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

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In this article, a Meta-cognitive Fully Complexvalued Fast Learning Classifier (Mc-FCFLC) for solving realvalued classification problems is presented. Mc-FCFLC consist of two components namely, a cognitive component and a metacognitive one. The cognitive component of Mc-FCFLC is a single hidden layer network (FCFLC) with a nonlinear input and hidden layer, and a linear output layer. The meta-cognitive component of Mc-FCFLC consist of a self-regulatory learning mechanism that chooses a best learning strategy among what-tolearn, when-to-learn and how -to-learn for a given sample. The sample is either deleted, used for adding a new neuron or else it is reserved for future use. Thus the architecture of Mc-FCFLC is constructed during the training process. The performance of the Mc-FCFLC is evaluated with the other complex-valued and a few best performing real-valued classifiers on a set of benchmark classification problems obtained from the UCI machine learning repository. Further, a practical acoustic emission signal classification problem has been addressed. Performance results demonstrate that Mc-FCFLC has better classification ability than the other classifiers existing in the literature.