{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:28:07Z","timestamp":1754155687293,"version":"3.41.2"},"reference-count":31,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2017,9,5]],"date-time":"2017-09-05T00:00:00Z","timestamp":1504569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PROG"],"published-print":{"date-parts":[[2017,9,5]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/prog-02-2016-0014","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T10:33:11Z","timestamp":1501237991000},"page":"278-297","source":"Crossref","is-referenced-by-count":2,"title":["Simultaneous instance and feature selection for improving prediction in special education data"],"prefix":"10.1108","volume":"51","author":[{"given":"Yenny","family":"Villuendas-Rey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen","family":"Rey-Bengur\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miltiadis","family":"Lytras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6250-4728","authenticated-orcid":false,"given":"Cornelio","family":"Y\u00e1\u00f1ez-M\u00e1rquez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oscar","family":"Camacho-Nieto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"4","key":"key2020120523354527700_ref001","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.eswa.2006.02.021","article-title":"A case-based reasoning system with the two-dimensional reduction technique for customer classification","volume":"32","year":"2007","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"key2020120523354527700_ref002","first-page":"72","article-title":"A machine learning approach to college drinking prediction and risk factor identification","volume":"4","year":"2013","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"8","key":"key2020120523354527700_ref003","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1111\/jcpp.12559","article-title":"Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi\u2010instrument fusion","volume":"57","year":"2016","journal-title":"Journal of Child Psychology and Psychiatry"},{"key":"key2020120523354527700_ref004","doi-asserted-by":"crossref","unstructured":"Cano, S., Collazos, C., Fardoun, H.M., Alghazzawi, D.M. and Albarakati, A. 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