Wrapper Fuzzy Approach with 3d Fast Convolution Neural Network (FCNN) Based Feature Selection in Protein Sequence Classification
Keywords:
Bioinformatics, protein sequences, classification, feature selection, noise removal, wrapper fuzzy, Classification using 3D- Fast Convolution neural network (3D- FCNN)Abstract
In research area, an emerging field is Bioinformatics in the past decades. Biological data storage and management was the definite motivation of bioinformatics and the tools for computation are developed and analyzed for enhancing their understanding. The data size is gathered under different project sequence is exponentially increased, that provides the problems for the methods of experiment. Newly sequenced protein and known functions proteins have gap and this gap is reduced by several techniques of computation incorporating classification and algorithms of clustering were presented in the past. The sequences of protein are classified into superfamilies exists in literature is useful for the prediction of structure and function of huge proteins that are discovered newly. The existing classification’s results are unacceptable because of larger feature size acquired by several approaches of feature encoding. This paper proposes noise removal technique depending on selection of feature for protein sequence classification. Here we use wrapper fuzzy model with fast convolution neural network (FCNN) for feature selection and remove the noise. This research involved in removal of noisy or unwanted data related to protein composite. To improve classification accuracy, wrapper fuzzy is utilized for selection of features. Wrapper algorithm involved in selection of protein features for accurate identification of protein composites. For classification we use 3D FCNN which can improve the accuracy of classification. The classification of protein proposed in this method proves momentous enhancement with respect to measuring the metrics of performance: accuracy, sensitivity, specificity, recall, F-measure, and etc.
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