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This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state\u2010of\u2010the\u2010art ones, in order to predict these three values. The obtained results were similar to those using state\u2010of\u2010the\u2010art procedures.<\/jats:p>","DOI":"10.1155\/2019\/4659809","type":"journal-article","created":{"date-parts":[[2019,1,8]],"date-time":"2019-01-08T23:59:37Z","timestamp":1546991977000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2963-7318","authenticated-orcid":false,"given":"Adrian","family":"Carballal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0413-5677","authenticated-orcid":false,"given":"Carlos","family":"Fernandez-Lozano","sequence":"additional","affiliation":[]},{"given":"Nereida","family":"Rodriguez-Fernandez","sequence":"additional","affiliation":[]},{"given":"Luz","family":"Castro","sequence":"additional","affiliation":[]},{"given":"Antonino","family":"Santos","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,1,8]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-7371-5.ch010"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.4018\/ijcicg.2014070104"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/11744078_23"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.12.106"},{"key":"e_1_2_11_5_2","doi-asserted-by":"crossref","unstructured":"KeY. 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