Fuzzy rule extraction for fruit data classification
Keywords:
Classification, Decision Tree, Fuzzy ID3, Fuzzy rulesAbstract
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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