{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:40:38Z","timestamp":1764978038098,"version":"3.46.0"},"reference-count":53,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), and\n                    <jats:italic>K<\/jats:italic>\n                    -nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (\u0394) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.\n                  <\/jats:p>","DOI":"10.1515\/jisys-2020-0064","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T18:39:34Z","timestamp":1634927974000},"page":"1026-1039","source":"Crossref","is-referenced-by-count":3,"title":["Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets"],"prefix":"10.1515","volume":"30","author":[{"given":"Rupali","family":"Tajanpure","sequence":"first","affiliation":[{"name":"GITAM University , Hyderabad , Telangana , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akkalakshmi","family":"Muddana","sequence":"additional","affiliation":[{"name":"GITAM University , Hyderabad , Telangana , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"2025120523322340370_j_jisys-2020-0064_ref_001","doi-asserted-by":"crossref","unstructured":"Flach P\n. Index. Machine learning: the art and science of algorithms that make sense of data. Cambridge: Cambridge University Press; 2012.","DOI":"10.1017\/CBO9780511973000"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_002","unstructured":"Han J\n, \nKamber M\n. Data mining: concepts and techniques. 3rd edn. Waltham: Morgan Kaufmann Publishers; 2006."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_003","doi-asserted-by":"crossref","unstructured":"Cai J\n, \nLuo J\n, \nWang S\n, \nYang S\n. Feature selection in machine learning: a new perspective. Neurocomputing. 2018 July;300:70\u20139.","DOI":"10.1016\/j.neucom.2017.11.077"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_004","doi-asserted-by":"crossref","unstructured":"Saeys Y\n, \nInza I\n, \nLarra\u00f1aga P\n. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007 Oct 1;23(19):2507\u201317. 10.1093\/bioinformatics\/btm344.","DOI":"10.1093\/bioinformatics\/btm344"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_005","doi-asserted-by":"crossref","unstructured":"Lu Q\n, \nLi X\n, \nDong Y\n. Structure preserving unsupervised feature selection. Neurocomputing. 2018;301:36\u201345.","DOI":"10.1016\/j.neucom.2018.04.001"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_006","doi-asserted-by":"crossref","unstructured":"Jain D\n, \nSingh V\n. Feature selection and classification systems for chronic disease prediction: a review. Egypt Inform J. 2018;19:179\u201389.","DOI":"10.1016\/j.eij.2018.03.002"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_007","doi-asserted-by":"crossref","unstructured":"Keerthi Vasan K\n, \nSurendiran B\n. Dimensionality reduction using principal component analysis for network intrusion detection. Perspect Sci. 2016;8:510\u20132.","DOI":"10.1016\/j.pisc.2016.05.010"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_008","doi-asserted-by":"crossref","unstructured":"Onan A\n, \nKorukoglu S\n. A feature selection model based on genetic rank aggregation for text sentiment classification. J Inf Sci. 2015;43(1):25\u201338. 10.1177\/0165551515613226.","DOI":"10.1177\/0165551515613226"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_009","doi-asserted-by":"crossref","unstructured":"Onan A\n. Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Comput Appl Eng Educ. 2020;29:572\u201389. 10.1002\/cae.22253.","DOI":"10.1002\/cae.22253"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_010","doi-asserted-by":"crossref","unstructured":"Onan A\n. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr Comput Pract Exp. 2020 June 29. 10.1002\/cpe.5909.","DOI":"10.1002\/cpe.5909"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_011","doi-asserted-by":"crossref","unstructured":"Onan A\n, \nTocoglu MA\n. Weighted word embeddings and clustering\u2010based identification of question topics in MOOC discussion forum posts. Comput Appl Eng Educ. 2020;29:675\u201389. 10.1002\/cae.22252.","DOI":"10.1002\/cae.22252"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_012","doi-asserted-by":"crossref","unstructured":"Onan A\n. Sentiment analysis in Turkish based on weighted word embeddings. 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey: IEEE; 2020. p. 1\u20134. 10.1109\/SIU49456.2020.9302182.","DOI":"10.1109\/SIU49456.2020.9302182"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_013","doi-asserted-by":"crossref","unstructured":"Onan A\n. Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering. IEEE Access. 2019;7:145614\u201333. 10.1109\/ACCESS.2019.2945911.","DOI":"10.1109\/ACCESS.2019.2945911"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_014","doi-asserted-by":"crossref","unstructured":"Semwal VB\n, \nSingha J\n, \nSharma P\n, \nChauhan A\n, \nBehera B\n. An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl. 2017;76:24457\u201375. 10.1007\/s11042-016-4110-y.","DOI":"10.1007\/s11042-016-4110-y"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_015","doi-asserted-by":"crossref","unstructured":"Gupta A\n, \nSemwal VB\n. Multiple task human gait analysis and identification: ensemble learning approach. In: \nMohanty SN\n, editor. Emotion and information processing. Cham: Springer; 2020. 10.1007\/978-3-030-48849-9_12.","DOI":"10.1007\/978-3-030-48849-9_12"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_016","doi-asserted-by":"crossref","unstructured":"Raj M\n, \nSemwal VB\n, \nNandi GC\n. Bidirectional association of joint angle trajectories for humanoid locomotion: the restricted Boltzmann machine approach. Neural Comput Appl. 2018;30:1747\u201355. 10.1007\/s00521-016-2744-3.","DOI":"10.1007\/s00521-016-2744-3"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_017","doi-asserted-by":"crossref","unstructured":"Semwal VB\n, \nMondal K\n, \nNandi GC\n. Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl. 2017;28:565\u201374. 10.1007\/s00521-015-2089-3.","DOI":"10.1007\/s00521-015-2089-3"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_018","doi-asserted-by":"crossref","unstructured":"Semwal VB\n, \nGaud N\n, \nNandi GC\n. Human gait state prediction using cellular automata and classification using ELM. In: \nTanveer M\n, \nPachori R\n, editors. Machine intelligence and signal analysis. Advances in intelligent systems and computing. Vol. 748. Singapore: Springer; 2019. 10.1007\/978-981-13-0923-6_12.","DOI":"10.1007\/978-981-13-0923-6_12"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_019","doi-asserted-by":"crossref","unstructured":"Onan A\n, \nTo\u00e7o\u011flu MA\n. A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access. 2021;9:7701\u201322. 10.1109\/ACCESS.2021.3049734.","DOI":"10.1109\/ACCESS.2021.3049734"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_020","doi-asserted-by":"crossref","unstructured":"Singh U\n, \nKedas S\n, \nPrasanth S\n, \nKumar A\n, \nSemwal VB\n, \nTikkiwal VA\n. Design of a recurrent neural network model for machine reading comprehension. Proc Comput Sci. 2020;167:1791\u2013800. 10.1016\/j.procs.2020.03.388. ISSN 1877-0509.","DOI":"10.1016\/j.procs.2020.03.388"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_021","doi-asserted-by":"crossref","unstructured":"Onan A\n. Mining opinions from instructor evaluation reviews: a deep learning approach. Comput Appl Eng Educ. 2020;28:117\u201338.","DOI":"10.1002\/cae.22179"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_022","doi-asserted-by":"crossref","unstructured":"Onan A\n, \nKorukoglu S\n, \nBulut H\n. Ensemble of keyword extraction methods and classifiers in text classification. Expert Syst Appl. 2016;57:232\u201347. 10.1016\/j.eswa.2016.03.045. ISSN 0957-4174.","DOI":"10.1016\/j.eswa.2016.03.045"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_023","doi-asserted-by":"crossref","unstructured":"Onan A\n, \nKoruko\u011flu S\n, \nBulut H\n. A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification. Inf Process Manag. 2017;53(4):814\u201333. 10.1016\/j.ipm.2017.02.008. ISSN 0306-4573.","DOI":"10.1016\/j.ipm.2017.02.008"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_024","doi-asserted-by":"crossref","unstructured":"Kontonatsios G\n, \nSpencer S\n, \nMatthew P\n, \nKorkontzelos I\n. Using a neural network-based feature extraction method to facilitate citation screening for systematic reviews. Expert Syst Appl. 2020;6:100030.","DOI":"10.1016\/j.eswax.2020.100030"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_025","unstructured":"Key facts about heart disease, the World Health Organization (WHO). Cardiovascular disease; June 2017 [Online]. Available: http:\/\/www.who.int\/mediacentre\/factsheets\/fs317\/en\/."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_026","doi-asserted-by":"crossref","unstructured":"Vivekanandan T\n, \nCh Sriman Narayana Iyengar N\n. Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput Biol Med. 2017;90:125\u201336.","DOI":"10.1016\/j.compbiomed.2017.09.011"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_027","doi-asserted-by":"crossref","unstructured":"Kumar V\n. Feature selection: a literature review. Smart Comput Rev. 2014;4:211\u201329. 10.6029\/smartcr.2014.03.007.","DOI":"10.6029\/smartcr.2014.03.007"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_028","doi-asserted-by":"crossref","unstructured":"Shrivastava P\n, \nShukla A\n, \nVepakomma P\n, \nBhansali N\n, \nVerma K\n. A survey of nature-inspired algorithms for feature selection to identify Parkinson\u2019s disease. Comput Methods Prog Biomed. 2017;139:171\u20139.","DOI":"10.1016\/j.cmpb.2016.07.029"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_029","unstructured":"Sudarson J\n, \nBalasaheb T\n. Improved artificial neural network (ANN) with aid of artificial bee colony (ABC) for medical data classification. Int J Bus Intell Data Min. 2017;1:1. 10.1504\/IJBIDM.2017.10010713."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_030","doi-asserted-by":"crossref","unstructured":"Peng Y\n, \nWu Z\n, \nJiang J\n. A novel feature selection approach for biomedical data classification. J Biomed Inform. 2010;43:15\u201323.","DOI":"10.1016\/j.jbi.2009.07.008"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_031","doi-asserted-by":"crossref","unstructured":"Xie J\n, \nWu J\n. Feature selection algorithm based on association rule mining method. Eighth IEEE\/ACIS ICCIS; 2009.","DOI":"10.1109\/ICIS.2009.103"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_032","doi-asserted-by":"crossref","unstructured":"Ferone A\n. Feature selection based on the composition of rough sets induced by feature granulation. Int J Approx Reason. 2018;101:276\u201392.","DOI":"10.1016\/j.ijar.2018.07.011"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_033","doi-asserted-by":"crossref","unstructured":"Liua J\n, \nLin Y\n. Feature selection based on the quality of information. Neurocomputing. 2017;225:11\u201322.","DOI":"10.1016\/j.neucom.2016.11.001"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_034","unstructured":"Oppenheim AV\n, \nSchafer RW\n. Digital signal processing. 1st edn. The University of Michigan, Pearson; Jan 12 1975."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_035","unstructured":"Proakis JG\n, \nManolakis DK\n. Digital signal processing: principles, algorithms, and applications. 3rd edn. South Asia: Pearson Publications; 1996."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_036","unstructured":"Dua D\n, \nGraff C\n. UCI Machine learning repository. Irvine, CA: The University of California, School of Information and Computer Science; 2019. http:\/\/archive.ics.uci.edu\/ml."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_037","doi-asserted-by":"crossref","unstructured":"He H\n, \nGarcia EA\n. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21:9.","DOI":"10.1109\/TKDE.2008.239"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_038","doi-asserted-by":"crossref","unstructured":"Tajanpure RR\n, \nJena S\n. Diagnosis of disease using feature decimation with multiple classifier system. In: \nDash S\n, \nDas S\n, \nPanigrahi B\n, editors. International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, 632. Singapore: Springer; 2018.","DOI":"10.1007\/978-981-10-5520-1_7"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_039","doi-asserted-by":"crossref","unstructured":"Solorio-Fern\u00e1ndez S\n, \nMart\u00ednez-Trinidad J\n, \nAriel Carrasco-Ochoa J\n. A new unsupervised spectral feature selection method for mixed data: a filter approach. Pattern Recognit. 2017;72:314\u201326.","DOI":"10.1016\/j.patcog.2017.07.020"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_040","doi-asserted-by":"crossref","unstructured":"Alk\u0131m E\n, \nG\u00fcrb\u00fcz E\n, \nK\u0131l\u0131\u00e7 E\n. A fast and adaptive automated disease diagnosis method with an innovative neural network model. Neural Netw. 2012;33:88\u201396.","DOI":"10.1016\/j.neunet.2012.04.010"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_041","doi-asserted-by":"crossref","unstructured":"Jain D\n, \nSingh V\n. Feature selection and classification systems for chronic disease prediction: a review. Egypt Inf J. 2018;19:179\u201389.","DOI":"10.1016\/j.eij.2018.03.002"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_042","doi-asserted-by":"crossref","unstructured":"Cura T\n. Use of support vector machines with a parallel local search algorithm for data classification and feature selection. Expert Syst Appl. 2020;145:113133.","DOI":"10.1016\/j.eswa.2019.113133"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_043","doi-asserted-by":"crossref","unstructured":"Yasmin G\n, \nDas AK\n, \nNayak J\n, \nPelusi D\n, \nDing W\n. Graph based feature selection investigating boundary region of rough set for language identification. Expert Syst Appl. 2020;158:113575.","DOI":"10.1016\/j.eswa.2020.113575"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_044","doi-asserted-by":"crossref","unstructured":"Song W\n, \nWang ST\n, \nLi CH\n. Parametric and nonparametric evolutionary computing with a content-based feature selection approach for parallel categorization. Expert Syst Appl. 2009;36:11934\u201343.","DOI":"10.1016\/j.eswa.2009.03.068"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_045","doi-asserted-by":"crossref","unstructured":"Dash M\n, \nLiu H\n. Consistency-based search in feature selection. Artif Intell. 2003;151:155\u201376.","DOI":"10.1016\/S0004-3702(03)00079-1"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_046","doi-asserted-by":"crossref","unstructured":"Weitschek E\n, \nFelici G\n, \nBertolazzi P\n. Clinical data mining: problems, pitfalls and solutions. 2013 24th International Workshop on Database and Expert Systems Applications, 1529-4188\/13 $2600@2013. IEEE; 2013. 10.1109\/DEXA.2013.42.","DOI":"10.1109\/DEXA.2013.42"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_047","doi-asserted-by":"crossref","unstructured":"Dash M\n, \nLiub H\n. Consistency-based search in feature selection. Artif Intell. 2003;151:155\u201376.","DOI":"10.1016\/S0004-3702(03)00079-1"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_048","doi-asserted-by":"crossref","unstructured":"Ferone A\n. Feature selection based on composition of rough sets induced by feature granulation. Int J Approx Reason. 2018;101:276\u201392.","DOI":"10.1016\/j.ijar.2018.07.011"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_049","unstructured":"Vijay Bhaskar S\n, \nGupta A\n, \nLalwani P\n. An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition. J Supercomput. 2021;103:1\u201324."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_050","unstructured":"Vijay Bhaskar S\n, \nNeha G\n, \nPraveen L\n, \nVishwanath B\n, \nAbhay Kumar A\n. Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor. Artif Intell Rev. 2021;1\u201321."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_051","doi-asserted-by":"crossref","unstructured":"Dua N\n, \nSingh SN\n, \nSemwal VB\n. Multi-input CNN-GRU based human activity recognition using wearable sensors. Computing. 2021;103:1461\u201378.","DOI":"10.1007\/s00607-021-00928-8"},{"key":"2025120523322340370_j_jisys-2020-0064_ref_052","unstructured":"Smith SW\n. The scientist and engineer\u2019s guide to digital signal processing. San Diego, Calif: California Technical Publishing; 1997."},{"key":"2025120523322340370_j_jisys-2020-0064_ref_053","doi-asserted-by":"crossref","unstructured":"Tarle B\n. Integrating multiple methods to enhance medical data classification. Evol Syst. 2020;11:133\u201342. 10.1007\/s12530-019-09272-x.","DOI":"10.1007\/s12530-019-09272-x"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0064\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0064\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:36:23Z","timestamp":1764977783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0064\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,9,22]]},"published-print":{"date-parts":[[2021,9,22]]}},"alternative-id":["10.1515\/jisys-2020-0064"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2020-0064","relation":{},"ISSN":["2191-026X"],"issn-type":[{"type":"electronic","value":"2191-026X"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}