{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T05:00:39Z","timestamp":1764133239289},"reference-count":31,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2019,5]]},"abstract":"<jats:p> This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances. <\/jats:p>","DOI":"10.1142\/s0218213019500131","type":"journal-article","created":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T04:07:01Z","timestamp":1559275621000},"page":"1950013","source":"Crossref","is-referenced-by-count":7,"title":["An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset"],"prefix":"10.1142","volume":"28","author":[{"given":"Ch. Sanjeev Kumar","family":"Dash","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar-751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajit Kumar","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Silicon Institute of Technology, Silicon Hills, Patia, Bhubaneswar-751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarat Chandra","family":"Nayak","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, CMR College of Engineering &amp; Technology (Autonomous), Hyderabad-501401, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satchidananda","family":"Dehuri","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore-756019, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,5,30]]},"reference":[{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(03)00079-1"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.05.019"},{"key":"p_10","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-4356(03)00170-7"},{"key":"p_11","first-page":"639","author":"Acuna E.","year":"2004","journal-title":"Heidelberg"},{"key":"p_15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.11.090"},{"key":"p_16","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0310-y"},{"key":"p_17","doi-asserted-by":"publisher","DOI":"10.1109\/2.144401"},{"key":"p_19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2013.08.006"},{"key":"p_20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2012.01.025"},{"key":"p_21","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2011.2161091"},{"key":"p_23","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2033580"},{"key":"p_24","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.11.048"},{"key":"p_25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.04.126"},{"key":"p_26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.03.066"},{"key":"p_29","first-page":"1","author":"Xu J.","year":"2010","journal-title":"Proc. of IEEE Int. Conf. on Communications (ICC ) ("},{"key":"p_30","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2011.35"},{"key":"p_32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2016.10.009"},{"key":"p_33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2015.07.015"},{"key":"p_34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.05.002"},{"key":"p_39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.02.050"},{"key":"p_40","doi-asserted-by":"publisher","DOI":"10.4310\/SII.2017.v10.n1.a2"},{"key":"p_42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.02.006"},{"key":"p_43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2006.05.003"},{"key":"p_44","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2015.03.002"},{"key":"p_45","doi-asserted-by":"publisher","DOI":"10.1080\/713827181"},{"key":"p_46","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.11.005"},{"key":"p_47","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2003.819265"},{"key":"p_48","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-010-9165-y"},{"key":"p_49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.10.007"},{"key":"p_50","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.04.021"},{"key":"p_52","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2015.1036221"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213019500131","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T17:11:59Z","timestamp":1565197919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213019500131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5]]},"references-count":31,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2019,5,30]]},"published-print":{"date-parts":[[2019,5]]}},"alternative-id":["10.1142\/S0218213019500131"],"URL":"https:\/\/doi.org\/10.1142\/s0218213019500131","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5]]}}}