Influence of Machine Learning vs. Ranking Algorithm on the Critical Dimension

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dc.contributor.authorSung, Andrew H.
dc.contributor.authorLiu, Qingzhong
dc.contributor.authorSuryakumar, Divya
dc.date.accessioned2022-01-21T17:39:31Z
dc.date.available2022-01-21T17:39:31Z
dc.date.issued2013-06
dc.descriptionArticle originally published in International Journal of Future Computer and Communication
dc.description.abstractThe critical dimension is the minimum number of features required for a learning machine to perform with “high” accuracy, which for a specific dataset is dependent upon the learning machine and the ranking algorithm. Discovering the critical dimension, if one exists for a dataset, can help to reduce the feature size while maintaining the learning machine’s performance. It is important to understand the influence of learning machines and ranking algorithms on critical dimension to reduce the feature size effectively. In this paper we experiment with three ranking algorithms and three learning machines on several datasets to study their combined effect on the critical dimension. Results show the ranking algorithm has greater influence on the critical dimension than the learning machine.
dc.description.sponsorshipICASA (Institute for Complex Additive Systems Analysis) of New Mexico Tech and the National Institute of Justice, U.S. Department of Justice (Award No. 2010-DN-BX-K223)
dc.identifier.citationSuryakumar D, Sung AH and Liu Q (2013). Influence of machine learning vs. ranking algorithm on the critical dimension, International Journal of Future Computer and Communication 2(3): 215-219.
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3256
dc.language.isoen
dc.publisherInternational Journal of Future Computer and Communication
dc.subjectCritical dimension
dc.subjectmachine learning
dc.subjectfeature selection
dc.subjectranking algorithm
dc.titleInfluence of Machine Learning vs. Ranking Algorithm on the Critical Dimension
dc.typeArticle

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