Classification Techniques in Data Mining: Classical and Fuzzy Classifiers

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Author(s): Ali Hosseinzadeh (Comprehensive Imam Hossein University, Iran)and S. A. Edalatpanah (Ayandegan Institute of Higher Education, Tonekabon, Iran)
Copyright: 2017
Pages: 36
Source title: Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices
Source Author(s)/Editor(s): Amal Kumar Adak (Jafuly Deshpran High School, India), Debashree Manna (Damda Jr. High School, India)and Monoranjan Bhowmik (Vidyasagar Teacher’s Training College, India)
DOI: 10.4018/978-1-5225-0914-1.ch007

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

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Classification Techniques in Data Mining: Classical and Fuzzy Classifiers

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