Developing Modified Classifier for Big Data Paradigm: An Approach Through Bio-Inspired Soft Computing

Abstract

The emerging applications of big data usher different blends of applications, where classification, accuracy and precision could be identified as major concern. The contemporary issues are also being emphasized as detecting multiple autonomous sources and unstructured trends of data. Therefore, it becomes mandatory to follow suitable classification and in addition to appropriate labelling of data is required to use relevant computational intelligent techniques. This is significant, where the movement of data is random and follows linked concept of data e.g. social network and blog data, transportation data and even supporting low-carbon road transport policies. It has been agreed by the research community whether only supervised classification techniques could be useful for such diversified imbalanced classification. Subsequently, the genesis of majority and minority class detection based on supervised features following conventional data mining principle. However, the classification of majority or positive class is over-sampled by taking each minority class sample. Definitely, significant computationally intelligent methodologies have been introduced. Following the philosophy of data science and big data, the heterogeneous classification, over-sampling, mis-labelled data features cannot be standardized with hard classification. Hence, conventional algorithm can be modified to support ensemble data set for precise classification under big and random data and that can be achieved through proposed monkey algorithm dynamic classification under imbalance. The proposed algorithm is not completely supervised rather it is blended with certain number of pre-defined examples and iterations. The approach could be more specific, when more numbers of soft computing methods, if they can be hybridized with bio-inspired algorithms.