Fuzzy rule extraction for fruit data classification

Authors

  • Devi SVSG Professor & Head, Department of MCA, K.S.R.M. College of Engineering, Kadapa – 516 003 (A.P.)

Keywords:

Classification, Decision Tree, Fuzzy ID3, Fuzzy rules

Abstract

Decision Tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic Decision Tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible Decision Trees have been designed and then rules are extracted for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data and with missing or noisy features. Recently, with the growing popularity of fuzzy representation in Decision Trees are introduced to deal with similar situations. Fuzzy representation bridges the gap between symbolic and non-symbolic data by linking qualitative linguistic terms with quantitative data. In this paper first Fuzzy Decision Tree for Fruit data classification is constructed and then the fuzzy classification rules are extracted.

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Published

2024-02-26

How to Cite

Devi, S. G. (2024). Fuzzy rule extraction for fruit data classification. COMPUSOFT: An International Journal of Advanced Computer Technology, 2(12), 400–403. Retrieved from https://ijact.in/index.php/j/article/view/70

Issue

Section

Original Research Article

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