Abstract
This article presents a Neutrosophic Rule-Based Classification System where neutrosophic logic (NL) is used to represent several forms of knowledge. The presented system generalizes the fuzzy rule-based classification by describing every logical variable with its truth, indeterminacy, and falsity degrees. Which are obtained from truth, indeterminacy, and falsity membership functions extracted from the fuzzy trapezoidal membership function. Then, it is followed by an extraction of the “IF-THEN” rules which used in the classification phase. Tests on different datasets; the Iris, the Wine, and the Wisconsin Diagnostic Breast Cancer (WDBC) have been done on the proposed neutrosophic rule based classification system. Comparisons between the proposed system and the fuzzy rule based classification system are done. The proposed system showed an accuracy of 94.7% compared with 89.5% of the fuzzy rule-based classification system, on average.
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