WITHDRAWN: Rider Weed Deep Residual Network-Based Incremental Model for Text Classification Using Multidimensional Features and MapReduce Framework

Abstract

The increasing demand for information and rapid growth of big data has dramatically increased textual data. The amount of different kinds of data has led to the overloading of information. For obtaining useful text information, the classification of texts is considered an imperative task. This paper develops a technique for text classification in big data using the MapReduce model. The goal is to design a hybrid optimization algorithm for classifying the text. Here, the pre-processing is done with the steaming process and stop word removal. In addition, the Extraction of imperative features is performed wherein SentiWordNet features, contextual features, and thematic features are generated. Furthermore, the selection of optimal features is performed using Tanimoto similarity. The Tanimoto similarity method estimates the similarity between the features and selects the relevant features with higher feature selection accuracy. After that, a deep residual network is utilized for dynamic text classification. The Adam algorithm trains the deep residual network. In addition, the dynamic learning is performed with the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines Invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). The method mentioned above is solved under the MapReduce framework. The proposed RIWO-based deep residual network outperformed other techniques with the highest True positive rate (TPR) of 85%, True negative rate (TNR) of 94%, and accuracy of 88.7%.