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However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real\u2010world problems. To address high\u2010dimensional and label\u2010missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label\u2010missing problem, a <jats:italic>k<\/jats:italic>\u2010nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm\u2010based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.<\/jats:p>","DOI":"10.1155\/2023\/4196920","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T15:05:10Z","timestamp":1677078310000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Semisupervised Bacterial Heuristic Feature Selection Algorithm for High\u2010Dimensional Classification with Missing 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