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The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.<\/jats:p>","DOI":"10.3233\/jifs-222994","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T12:25:04Z","timestamp":1682684704000},"page":"719-730","source":"Crossref","is-referenced-by-count":2,"title":["A novel negative selection algorithm with hyper-rectangle detectors based on full coverage of state space for anomaly detection"],"prefix":"10.1177","volume":"45","author":[{"given":"Ming","family":"Gu","sequence":"first","affiliation":[{"name":"School of Petroleum Engineering, Changzhou University, Changzhou, P.R. China"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Petroleum Engineering, Changzhou University, Changzhou, P.R. 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