{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:54:27Z","timestamp":1776088467240,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Not all data features are crucial for uncovering hidden knowledge within various datasets, making the reduction of their dimensional attributes a significant area of interest. In this work, a new meta-heuristic algorithm IGWO-RF which is a combination of an improved gray wolf optimization (GWO) algorithm and random forest (RF) is suggested for feature selection problems. In the improved GWO, a nonlinear variable is introduced to establish an acceptable balance between exploration and mining processes. Moreover, the position of beta wolves in the GWO algorithm is used more in deciding to move toward the goal. In this way, inspired by the genetic algorithm, alpha and beta wolves are considered as parents, and 2 children are produced using the crossover, which after checking their fitness is either added to the population and causes the delta wolves to be eliminated or does not affect the process. The RF algorithm is used to calculate and update the fitness value in each iteration of the IGWO-FR method. The proposed technique was assessed through the average number of selected features, average classification accuracy, and best fitness. Additionally, the performance of the proposed algorithm was compared with several popular wrapper evolutionary-based feature selection techniques. Upon experiments and comparisons, it was evident that the suggested IGWO-FR method yielded the most superior results across all the datasets evaluated from the UCI machine learning (ML) repository. Therefore, the utilization of this algorithm for pattern classification was proven to be effective in enhancing classification performance.<\/jats:p>","DOI":"10.31449\/inf.v50i1.10635","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:49Z","timestamp":1776085549000},"source":"Crossref","is-referenced-by-count":0,"title":["IGWO-RF: An improved gray wolf optimization algorithm integrated with random forest for feature selection problems"],"prefix":"10.31449","volume":"50","author":[{"given":"Zhichao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaiyi","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,13]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10635\/6604","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10635\/6604","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:05:49Z","timestamp":1776085549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,4,13]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i1.10635","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}