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In recent days Data mining gained a large amount importance because it enables modeling and knowledge extraction from the abundance of data which is available. In which Decision trees are powerful and popular tools for categorization and predicting knowledge discovery. Due to drawback of sharp decision boundaries decision tree algorithms are not that much efficiently implemented to define real time classification problem. An another important parameter to be considered is like when the results are larger and deeper for a decision tree it leads to inexplicable induction rules. In this paper, the authors are proposing a Fuzzy Supervised learning in Quest Decision Tree (FS-DT) algorithm where they focused to design a fuzzy decision boundary instead of a crisp decision boundary.
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