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Methods: In this study, we implemented the PS-Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means an acceptable degree of reliability. Conclusions: Our results show that PS-Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI).<\/jats:p>","DOI":"10.3233\/jifs-235053","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T12:13:22Z","timestamp":1699013602000},"page":"529-541","source":"Crossref","is-referenced-by-count":0,"title":["PS-Merge operator in the classification of gait biomarkers: A preliminary approach to eXplainable Artificial Intelligence"],"prefix":"10.1177","volume":"46","author":[{"given":"Eddy","family":"S\u00e1nchez-DelaCruz","sequence":"first","affiliation":[{"name":"Artificial Intelligence Lab., National Technological, Misantla Campus, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sameem","family":"Abdul-Kareem","sequence":"additional","affiliation":[{"name":"UCSI University, 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