{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:06:00Z","timestamp":1771668360807,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Blood cancer is a rising issue in the past decade, and early detection is a must for early intervention. Traditional techniques for diagnosing blood cancer include high expense, long processes, and medical professionals and a variety of tests. Hence, an effective prediction model with high accuracy is a must. This study presents a robust leukemia multiclass classification framework leveraging advanced ML (machine learning) techniques. Addressing key challenges such as class imbalance, high-dimensional gene expression data, and feature selection. This study presents an integrated approach for data balancing by combining the Synthetic Minority Oversampling Technique (SMOTE) with nonlinear interpolation. A hybrid feature selection model utilizing Principal Component Analysis (PCA) on Linear Discriminant Analysis (LDA) is implemented to enhance classification performance. Experimental results indicate that using SMOTE with PCA+LDA on Random Forest classifiers outperforms traditional methods, achieving 98% accuracy in leukemia multiclass classification.<\/jats:p>","DOI":"10.31449\/inf.v50i6.11956","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:23Z","timestamp":1771665863000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Leukemia Subtype Classification Using SMOTE and Hybrid Feature Selection in Microarray Data"],"prefix":"10.31449","volume":"50","author":[{"given":"Chaitra","family":"P C","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R Saravana","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11956\/6483","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11956\/6483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:23Z","timestamp":1771665863000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i6.11956","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}