Prediction of Dumping a Product in Textile Industry

Fuzzy Decision Trees (FDT's) are one of the most popular choices for learning and reasoning from dataset. They have undergone a number of alterations to language and measurement uncertainties. However, they are poor in classification accuracy. In this paper, Neuro -fuzzy decision tree ( a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. In the forward cycle fuzzy decision tree is constructed and in the feedback cycle, parameters of fuzzy decision tree have been adapted using stochastic gradient descent algorithm by traversing back from leaf to ischolar_main nodes. In this paper, the system may predict whether a product in dumped or not for the textile industry is explained.