{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:42:08Z","timestamp":1702600928511},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>The outcome of data classification is affected not only by the goodness of the classifier, but also by the complexity of the data itself. As a result, quantifying the complexity of the data itself can serve as a reference point for evaluating categorization models. Current approaches to quantifying data complexity overlook the significance of local data complexity in favor of a global viewpoint. In this research, we present a KNN-based data classification complexity measure with dynamic optimisation k-value (C2M_kNN) that gives greater weight to border sample classification difficulty. First, using dynamic optimization, the best k-value for k-Nearest-Neighbors is determined for each dataset. The samples that have a significant impact on classification complexity are next filtered by the kNN algorithm, and the classification complexity of the data is finally assessed. Based on created and real datasets, C2M_kNN performs better in experiments than 11 traditional data categorization complexity metrics.<\/jats:p>","DOI":"10.3233\/faia231084","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:09:59Z","timestamp":1702566599000},"source":"Crossref","is-referenced-by-count":0,"title":["A Complexity Measure for Data Classification Based on KNN with Dynamic Optimal K-Value Finding"],"prefix":"10.3233","author":[{"given":"Qiangkui","family":"Leng","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfu","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:10:02Z","timestamp":1702566602000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231084","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}