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Classification Techniques in Data Mining: Classical and Fuzzy Classifiers

Classification Techniques in Data Mining: Classical and Fuzzy Classifiers

Ali Hosseinzadeh, S. A. Edalatpanah
Copyright: © 2017 |Pages: 36
ISBN13: 9781522509141|ISBN10: 1522509143|EISBN13: 9781522509158
DOI: 10.4018/978-1-5225-0914-1.ch007
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MLA

Hosseinzadeh, Ali, and S. A. Edalatpanah. "Classification Techniques in Data Mining: Classical and Fuzzy Classifiers." Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices, edited by Amal Kumar Adak, et al., IGI Global, 2017, pp. 153-188. https://doi.org/10.4018/978-1-5225-0914-1.ch007

APA

Hosseinzadeh, A. & Edalatpanah, S. A. (2017). Classification Techniques in Data Mining: Classical and Fuzzy Classifiers. In A. Adak, D. Manna, & M. Bhowmik (Eds.), Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices (pp. 153-188). IGI Global. https://doi.org/10.4018/978-1-5225-0914-1.ch007

Chicago

Hosseinzadeh, Ali, and S. A. Edalatpanah. "Classification Techniques in Data Mining: Classical and Fuzzy Classifiers." In Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices, edited by Amal Kumar Adak, Debashree Manna, and Monoranjan Bhowmik, 153-188. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0914-1.ch007

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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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