RETRACTED ARTICLE: A wrapper based feature selection in bone marrow plasma cell gene expression data

The Microarray technology permits simultaneous monitoring of several gene expressions for every sample. But unfortunately, this classification of such samples into the distinct classes has often been found to difficult as the actual number of genes (the features) will to a great extent exceed the actual number of the samples. There is a high dimensionality in gene and its expression data which is a huge challenge in many of the problems of classification. Cloud computing is that popular concept of computing which performs a processing of a data of huge volumes by making use of highly accessible resources that are geographically distributed and accessed by the users based on the policy of pay as per use. In spite of the several steps in that of the B Cell that are now elucidate the last few stages of that of the plasma cell (PC) based differentiation that have not been understood yet. The PCs had generated at the time of primary and humoral immune responses that have started their differentiation within their light zones for the germinal centers of such light zones of that of the lymph nodes or the ones in the red pulp of the spleen having a life span of about few days. The selection of the features will aim at the maintenance of their original features of such data and at this time it will seek at identifying their main features and will weed out all those that are irrelevant for building of a learning model that is impactful. Identifying one global maximum will be Non-deterministic Polynomial (NP)-hard and if the criterion is decomposable or possesses the properties that can make some approximate type of optimization easy. The artificial bee colony (ABC) is that one that is used widely and applied for solving all real world problems. The stochastic diffusion search (SDS) will be an efficient multi-agent global search based technique of optimization applied to that of several problems and here the hybrid ABC with that of the SDS feature selection will be proposed and these images will be grouped as either normal or abnormal PC of the bone marrow that is based on this gene expression data. The support vector machine is that algorithms of supervised learning that is capable of being able to solve the complex problems in classification. This proposed method of gene selection will yield a comparable performance for the classification on being compared to that of the currently existing classifiers providing another new insight in case of feature selection.