Optimized deep belief network and entropy-based hybrid bounding model for incremental text categorization

Abstract

Purpose This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier. Design/methodology/approach At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents. Findings The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively. Originality/value This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review

“…It is for creating a better algorithm that optimizes the solution (feature subsets) to enhance the quality of the initial candidate solutions using the local search strategy. Thus, an improved algorithm aids in avoiding local optima trapping, avoiding premature convergence, efficiently and effectively exploring the search space, and making excellent decisions [10], [51], [52].…”

“…To perform a multi-label classification, the ANN was used. Srilakshmi et al [51], proposed a classification method based on three processing steps: VSM for feature extraction followed by the FS process that is performed using a hybridization method, and then a classifier is used for the classification. The proposed FS hybridizes the grasshopper optimization algorithm and the Crow Search Algorithm (GCOA).…”

“…Srilakshmi et al [51], suggested GCOA by merging the GOA and CSA algorithms. The CSA is derived from the incentive gained from crows' clever behavior in seeking prey and locating it using memory.…”

“…In [65], however, the performance of the IABCM algorithm was improved using a semantic representation from the ODP knowledge-based method. Another direction of study is to consider the feature weights before applying the FS methods as it is proven to improve accuracy [1], [43], [47], [51].…”

Incremental Learning Framework for Mining Big Data Stream

“…The data stream is the manifestation of big data that is characterized by five dimensions (5 V), namely value, variety, veracity, velocity, and volume. Data stream mining is the methodology used to deal with the data analysis of a huge volume of data samples in an ordered sequence [7][8][9][10][11][12][13]. Incremental learning follows the paradigm of machine learning methods.…”