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Recently, a general performance score (GPS) comprising different combinations of traditional performance measures (TPMs) was proposed. However, it indiscriminately assigns equal importance to each measure, often leading to inconsistencies. To overcome the shortcomings of GPS, we introduce an enhanced metric called the Weighted General Performance Score (W-GPS) that considers each measure\u2019s coefficient of variation (CV) and subsequently assigns weights to that measure based on its CV value. Considering consistency as a criterion, we found that W-GPS outperformed GPS in the above-mentioned classification scenarios. Further, considering W-GPS with different weighted combinations of TPMs, it was observed that no demarcation of these combinations that work best in a given scenario exists. Thus, W-GPS offers flexibility to the user to choose the most suitable combination for a given scenario.<\/jats:p>","DOI":"10.3233\/idt-240465","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T10:55:35Z","timestamp":1721991335000},"page":"2033-2054","source":"Crossref","is-referenced-by-count":0,"title":["Robust weighted general performance score for various classification scenarios"],"prefix":"10.1177","volume":"18","author":[{"given":"Gaurav","family":"Pandey","sequence":"first","affiliation":[{"name":"Research and Analytics Division, Analyttica Datalab, Whitefield, Bangalore, Karnataka, India"},{"name":"School of Artificial Intelligence and Data Science, IIT Jodhpur, Jodhpur, Rajasthan, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rashika","family":"Bagri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajan","family":"Gupta","sequence":"additional","affiliation":[{"name":"Research and Analytics Division, Analyttica Datalab, Whitefield, Bangalore, Karnataka, India"},{"name":"Artificial Intelligence and Innovation Lab, Universidad Autonoma de Tamaulipas, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Rajpal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoj","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hans Raj College, University of Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveen","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"10","key":"10.3233\/IDT-240465_ref1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A few useful things to know about machine learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Communications of the ACM"},{"key":"10.3233\/IDT-240465_ref2","unstructured":"Duda RO, Hart PE, et al. 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