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The authors consider the ranking as a membership value of this feature in Fuzzification of Euclidian calculations rather than using the crisp concept of feature selection, which selects some features and ignores others. Experimental results proved that applying the fuzzy value of memberships to Euclidian calculations in the FCM and SVM techniques has better accuracy than the ordinary calculating method and just ignoring the unselected features.<\/p>","DOI":"10.4018\/ijssmet.2014100103","type":"journal-article","created":{"date-parts":[[2015,1,28]],"date-time":"2015-01-28T12:04:48Z","timestamp":1422446688000},"page":"29-43","source":"Crossref","is-referenced-by-count":7,"title":["Fuzzification of Euclidean Space Approach in Machine Learning Techniques"],"prefix":"10.4018","volume":"5","author":[{"given":"Mostafa A.","family":"Salama","sequence":"first","affiliation":[{"name":"British University, Cairo, Egypt"}]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[{"name":"Cairo University, Cairo, Egypt & Scientific Research Group in Egypt (SRGE), Egypt"}]}],"member":"2432","reference":[{"key":"ijssmet.2014100103-0","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2007.430.435"},{"key":"ijssmet.2014100103-1","doi-asserted-by":"crossref","unstructured":"Bhargavi, P. and Jyothi, S. 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