2020
DOI: 10.1108/ijwis-03-2020-0015
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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 classificat… Show more

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Cited by 9 publications
(11 citation statements)
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“…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].…”
Section: B Discussion Of Findings Based On the Research Questionsmentioning
confidence: 99%
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“…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].…”
Section: B Discussion Of Findings Based On the Research Questionsmentioning
confidence: 99%
“…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).…”
Section: C: Hybridize Metaheuristic Methods (Combining Between Two Me...mentioning
confidence: 99%
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“…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.…”
Section: Introductionmentioning
confidence: 99%