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Fuzzy soft information measures and their applications in dimension reduction and pattern recognition

机译：模糊软信息及其措施在降维中的应用和模式识别

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Abstract Soft set theory introduced by Molodtsov (Comput Math Appl 37:19–31, 1999) is an effective tool for solving realistic problems related to engineering, social sciences, medical sciences, and business. In the environment of vagueness and comprehension, Maji et al. (J Fuzzy Math 9:677–692, 2001) defined a new model known as a fuzzy soft set by hybridizing a fuzzy set with soft set. Recently, information measures for fuzzy soft sets have gained attention from researchers. In the present paper, two fuzzy soft information measures are proposed with the verification of their validity. Their applications in data dimension reduction and pattern recognition are also studied in detail and illustrated with numerical examples.
机译：文摘Molodtsov软集合理论(第一版数学:37:19-31,1999)是一种有效的解决现实问题的工具工程学、社会科学、医学科学、和业务。理解,Maji et al。(J模糊数学9:677 - 692, 2001)定义了一个称为新模型组合一个模糊集,模糊软集软集合。模糊软集从引起了公众的关注研究人员。提出了措施与信息验证的有效性。应用程序在数据降维模式识别也详细研究并给出数值例子进行说明。

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