Fuzzy SLIQ Decision Tree for Quantitative Data-sets
Decision trees are powerful and popular tools for classification and prediction in knowledge discovery and data mining this area gained that much importance because it enables modeling and knowledge extraction from the abundance of data available. Due to limitation of sharp decision boundaries decision tree algorithms are not that much effectively implemented to define real time classification problem. When the results are larger and deeper for a decision tree it leads to inexplicable induction rules which is another important parameter to be considered. In this paper we are proposing a fuzzy super vised learning in Quest decision tree (FS-DT) algorithm where we focused to design a fuzzy decision boundary instead of a crisp decision boundary. The SLIQ decision tree algorithm which is used to construct a fuzzy binary decision tree is modified here by FS-DT to reduce the size of the decision tree, using several real-life datasets taken from the UCI Machine Learning Repository performance of the FS-DT algorithm is compared with SLIQ .Several comparisons between SLIQ and FS-DT has been proposed in this paper which out comes the constraints of Traditional decision tree algorithms.
