{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:23:11Z","timestamp":1781108591674,"version":"3.54.1"},"reference-count":37,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<p>With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along with classification have become an intriguing subject in the bioinformatics field. It holds numerous genes with few samples that make it arduous to examine and process. A novel strategy aimed at the classification of GE dataset as well as clustering-centered feature selection is proposed in the paper. The proposed technique first preprocesses the dataset using normalization, and later, feature selection was accomplished with the assistance of feature clustering support vector machine (FCSVM). It has two phases, gene clustering and gene representation. To make the chose top-positioned features worthy for classification, feature reduction is performed by utilizing SVM-recursive feature elimination (SVM-RFE) algorithm. Finally, the feature-reduced data set was classified using artificial neural network (ANN) classifier. When compared with some recent swarm intelligence feature reduction approach, FCSVM-ANN showed an elegant performance.<\/p>","DOI":"10.4018\/ijsir.2020010104","type":"journal-article","created":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T11:43:39Z","timestamp":1571744619000},"page":"65-86","source":"Crossref","is-referenced-by-count":13,"title":["Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection"],"prefix":"10.4018","volume":"11","author":[{"given":"Audu Musa","family":"Mabu","sequence":"first","affiliation":[{"name":"SHUATS, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3456-6980","authenticated-orcid":true,"given":"Rajesh","family":"Prasad","sequence":"additional","affiliation":[{"name":"African University of Science and Technology, Abuja, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghav","family":"Yadav","sequence":"additional","affiliation":[{"name":"SHUATS, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSIR.2020010104-0","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56154-7_34"},{"key":"IJSIR.2020010104-1","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/604910"},{"key":"IJSIR.2020010104-2","doi-asserted-by":"crossref","unstructured":"Alshamlan, Badr, & Alohali. (2016). ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification. International Journal of Machine Learning and Computing, 6(3).","DOI":"10.18178\/ijmlc.2016.6.3.596"},{"key":"IJSIR.2020010104-3","first-page":"133","author":"P.Amit","year":"2014","journal-title":"Dimension Reduction of Gene Expression Data for Designing Optimized Rule Base Classifier. In Recent Advances in Information Technology"},{"key":"IJSIR.2020010104-4","first-page":"92","article-title":"Dimensionality reduction via isomap with lock-step and elastic measures for time series gene expression classification.","author":"O.Carlotta","year":"2013","journal-title":"European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics"},{"key":"IJSIR.2020010104-5","first-page":"1","article-title":"Gene selection for microarray data classification via subspace learning and manifold regularization.","author":"T.Chang","year":"2017","journal-title":"Medical & Biological Engineering & Computing"},{"key":"IJSIR.2020010104-6","first-page":"185","article-title":"Document Clustering using Particle Swarm Optimization","author":"X.Cui","year":"2005","journal-title":"IEEE Swarm Intelligence Symposium"},{"key":"IJSIR.2020010104-7","first-page":"507","article-title":"Complex Dynamics in Tumor Gene Regulatory Networks: Oncogenesis Dynamics Driven by \u201cGenes Gone Crazy\u201d.","author":"I. L.George","year":"2016","journal-title":"XIV Mediterranean Conference on Medical and Biological Engineering and Computing"},{"key":"IJSIR.2020010104-8","first-page":"389","article-title":"A comparative study of cancer classification methods using microarray gene expression profile.","author":"A.Hala","year":"2014","journal-title":"Proceedings of the First International Conference on Advanced Data and Information Engineering"},{"key":"IJSIR.2020010104-9","first-page":"300","article-title":"Selection and classification of gene expression data using a MF-GA-TS-SVM approach.","author":"A. L.Hern\u00e1ndez-Montiel","year":"2014","journal-title":"International Conference on Intelligent Computing"},{"key":"IJSIR.2020010104-10","first-page":"732","article-title":"Classifying Non-linear Gene Expression Data Using a Novel Hybrid Rotation Forest Method.","author":"L.Huijuan","year":"2017","journal-title":"International Conference on Intelligent Computing"},{"key":"IJSIR.2020010104-11","first-page":"503","author":"S. S.Jacophine","year":"2016","journal-title":"Relevant Gene Selection and Classification of Leukemia Gene Expression Data. In Emerging Research in Computing, Information, Communication and Applications"},{"key":"IJSIR.2020010104-12","first-page":"484","article-title":"A New gene expression profiles classifying approach based on neighborhood rough set and probabilistic neural networks Ensemble.","author":"Y.Jiang","year":"2013","journal-title":"International Conference on Neural Information Processing"},{"key":"IJSIR.2020010104-13","first-page":"468","article-title":"Semi-supervised SVM-based feature selection for cancer classification using microarray gene expression data.","author":"C. A.Jun","year":"2015","journal-title":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems"},{"key":"IJSIR.2020010104-14","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-0491-6_54"},{"key":"IJSIR.2020010104-15","first-page":"385","article-title":"Multiple gene sets for cancer classification using gene range selection based on random forest.","author":"M.Kohbalan","year":"2013","journal-title":"Asian Conference on Intelligent Information and Database Systems"},{"key":"IJSIR.2020010104-16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.08.032"},{"key":"IJSIR.2020010104-17","first-page":"65","author":"J.Liu","year":"2007","journal-title":"An Improved FMM Neural Network for Classification of Gene Expression Data. In Fuzzy Information and Engineering"},{"key":"IJSIR.2020010104-18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.07.080"},{"key":"IJSIR.2020010104-19","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/3572705"},{"key":"IJSIR.2020010104-20","first-page":"50","article-title":"K-Means Clustering with Infinite Feature Selection for Classification Tasks in Gene Expression Data.","author":"A. R.Muhammad","year":"2017","journal-title":"International Conference on Practical Applications of Computational Biology & Bioinformatics"},{"key":"IJSIR.2020010104-21","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720018500063"},{"issue":"3","key":"IJSIR.2020010104-22","first-page":"70","article-title":"GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.","volume":"9","author":"D.Narumol","year":"2016","journal-title":"BMC Medical Genomics"},{"key":"IJSIR.2020010104-23","first-page":"286","article-title":"Gene network modules-based liner discriminant analysis of microarray gene expression data.","author":"H.Pingzhao","year":"2011","journal-title":"International Symposium on Bioinformatics Research and Applications"},{"key":"IJSIR.2020010104-24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2012.03.008"},{"key":"IJSIR.2020010104-25","doi-asserted-by":"publisher","DOI":"10.1504\/IJDMB.2017.084026"},{"key":"IJSIR.2020010104-26","doi-asserted-by":"publisher","DOI":"10.1016\/j.gdata.2015.04.027"},{"key":"IJSIR.2020010104-27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.11.026"},{"key":"IJSIR.2020010104-28","first-page":"1","article-title":"Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.","author":"C.Sankhadeep","year":"2017","journal-title":"Medical & Biological Engineering & Computing"},{"key":"IJSIR.2020010104-29","doi-asserted-by":"publisher","DOI":"10.1007\/s11814-015-0255-z"},{"key":"IJSIR.2020010104-30","first-page":"67","article-title":"Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data.","author":"W.Shuaiqun","year":"2016","journal-title":"International Conference on Intelligent Computing"},{"key":"IJSIR.2020010104-31","first-page":"864","article-title":"SVM-based tumor classification with gene expression data.","author":"W.Shulin","year":"2006","journal-title":"International conference on advanced data mining and applications"},{"key":"IJSIR.2020010104-32","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2017.04.002"},{"key":"IJSIR.2020010104-33","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720017500226"},{"key":"IJSIR.2020010104-34","first-page":"127","author":"K. S.Udhaya","year":"2014","journal-title":"Improved bijective-soft-set-based classification for gene expression data. In Computational Intelligence, Cyber Security and Computational Models"},{"key":"IJSIR.2020010104-35","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.03.178"},{"issue":"1","key":"IJSIR.2020010104-36","first-page":"17","article-title":"Informative gene selection and the direct classification of tumors based on relative simplicity","volume":"17","author":"C.Yuan","year":"2016","journal-title":"BMC Bioinformatics"}],"container-title":["International Journal of Swarm Intelligence Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=240630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T21:55:58Z","timestamp":1651874158000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSIR.2020010104"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijsir.2020010104","relation":{},"ISSN":["1947-9263","1947-9271"],"issn-type":[{"value":"1947-9263","type":"print"},{"value":"1947-9271","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}