{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:47:30Z","timestamp":1777682850347,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHS"],"published-print":{"date-parts":[[2021,7,7]]},"abstract":"<jats:p>One of the effective text categorization methods for learning the large-scale data and the accumulated data is incremental learning. The major challenge in the incremental learning is improving the accuracy as the text document consists of numerous terms. In this research, a incremental text categorization method is developed using the proposed Spider Grasshopper Crow Optimization Algorithm based Deep Belief Neural network (SGrC-based DBN) for providing optimal text categorization results. The proposed text categorization method has four processes, such as are pre-processing, feature extraction, feature selection, text categorization, and incremental learning. Initially, the database is pre-processed and fed into vector space model for the extraction of features. Once the features are extracted, the feature selection is carried out based on mutual information. Then, the text categorization is performed using the proposed SGrC-based DBN method, which is developed by the integration of the spider monkey optimization (SMO) with the Grasshopper Crow Optimization Algorithm (GCOA) algorithm. Finally, the incremental text categorization is performed based on the hybrid weight bounding model that includes the SGrC and Range degree and particularly, the optimal weights of the Range degree model is selected based on SGrC. The experimental result of the proposed text categorization method is performed by considering the data from the Reuter database and 20 Newsgroups database. The comparative analysis of the text categorization method is based on the performance metrics, such as precision, recall and accuracy. The proposed SGrC algorithm obtained a maximum accuracy of 0.9626, maximum precision of 0.9681 and maximum recall of 0.9600, respectively when compared with the existing incremental text categorization methods.<\/jats:p>","DOI":"10.3233\/jhs-210659","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T14:40:33Z","timestamp":1624372833000},"page":"183-202","source":"Crossref","is-referenced-by-count":1,"title":["Incremental text categorization based on hybrid optimization-based deep belief neural network"],"prefix":"10.1177","volume":"27","author":[{"given":"V.","family":"Srilakshmi","sequence":"first","affiliation":[{"name":"CSE, JNTUA, Anantapur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Anuradha","sequence":"additional","affiliation":[{"name":"CSE, GRIET, Hyderabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Shoba Bindu","sequence":"additional","affiliation":[{"name":"CSE, JNTUA, Anantapur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"81","key":"10.3233\/JHS-210659_ref2","first-page":"4033","article-title":"Arabic text categorization using classification rule mining","volume":"6","author":"Al-Diabat","year":"2012","journal-title":"Applied Mathematical Sciences"},{"issue":"1","key":"10.3233\/JHS-210659_ref3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s12293-013-0128-0","article-title":"Spider Monkey Optimization algorithm for numerical optimization","volume":"6","author":"Chand Bansal","year":"2014","journal-title":"Memetic Computing"},{"issue":"3","key":"10.3233\/JHS-210659_ref4","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1016\/j.eswa.2008.06.054","article-title":"Feature selection for text classification with na\u00efve Bayes","volume":"36","author":"Chen","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.3233\/JHS-210659_ref5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.diin.2011.04.002","article-title":"Author gender identification from text","volume":"8","author":"Cheng","year":"2011","journal-title":"Digital Investigation"},{"key":"10.3233\/JHS-210659_ref7","doi-asserted-by":"crossref","unstructured":"G.\u00a0D\u2019Angelo and S.\u00a0Rampone, Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm, in: Proceedings of the IEEE Conference on Metrology for Aerospace (MetroAeroSpace), 2014.","DOI":"10.1109\/MetroAeroSpace.2014.6865959"},{"key":"10.3233\/JHS-210659_ref8","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1016\/j.eswa.2012.02.053","article-title":"SMS spam filtering: Methods and data","volume":"39","author":"Delany","year":"2012","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JHS-210659_ref9","unstructured":"R.\u00a0Elhassan and M.\u00a0Ahmed, Arabic text classification review, International Journal of Computer Science and Software Engineering (IJCSSE) 4 (2015), 1."},{"key":"10.3233\/JHS-210659_ref10","doi-asserted-by":"crossref","unstructured":"M.\u00a0Ficco, F.\u00a0Palmieri and A.\u00a0Castiglione, Modeling security requirements for cloud-based system development, in: Concurrency and Computation: Practice and Experience, 2014.","DOI":"10.1002\/cpe.3402"},{"key":"10.3233\/JHS-210659_ref11","doi-asserted-by":"crossref","unstructured":"A.S.\u00a0Ghareb, A.R.\u00a0Hamdan and A.A.\u00a0Bakar, Text associative classification approach for mining Arabic data set, in: Data Mining and Optimization (DMO), 2012, pp.\u00a0114\u2013120.","DOI":"10.1109\/DMO.2012.6329808"},{"issue":"12","key":"10.3233\/JHS-210659_ref12","doi-asserted-by":"publisher","first-page":"10967","DOI":"10.1016\/j.eswa.2012.03.027","article-title":"Automated text classification using a dynamic artificial neural network model","volume":"39","author":"Ghiassi","year":"2012","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JHS-210659_ref13","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"issue":"1","key":"10.3233\/JHS-210659_ref14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s00521-016-2401-x","article-title":"Text classification based on deep belief network and softmax regression","volume":"29","author":"Jiang","year":"2016","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/JHS-210659_ref15","doi-asserted-by":"crossref","unstructured":"T.\u00a0Joachims, Text categorization with Support Vector Machines: Learning with many relevant features, in: Machine Learning: ECML-98. 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