{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:17:01Z","timestamp":1740147421072,"version":"3.37.3"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T00:00:00Z","timestamp":1539129600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Program Fund of Science and Technology Department Jilin province, China","award":["20150414051GH"],"award-info":[{"award-number":["20150414051GH"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Multimedia"],"published-print":{"date-parts":[[2018,10,10]]},"abstract":"<jats:p>To discover the influence of the commercial videos\u2019 low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos\u2019 evaluation mostly after analyzing the source data and the audiences\u2019 evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos\u2019 feature selection and the most influential feature set was obtained.<\/jats:p>","DOI":"10.1155\/2018\/2056381","type":"journal-article","created":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T23:33:20Z","timestamp":1539214400000},"page":"1-20","source":"Crossref","is-referenced-by-count":1,"title":["Commercial Video Evaluation via Low-Level Feature Extraction and Selection"],"prefix":"10.1155","volume":"2018","author":[{"given":"Xiangmin","family":"Lun","sequence":"first","affiliation":[{"name":"College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China"},{"name":"School of Automation Engineering, Northeast Electric Power University, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, Northeast Electric Power University, Jilin, 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