Implementation with Performance Evaluation of Decision Tree Classifier for Uncertain Data: Literature Review

To extract meaningful and non-negligible facts from large amounts of data for the extraction of patterns, anomalies, and correspondence information from large databases, data mining is used. Uncertain Data Implementation and Decision Tree Classifier Performance Evaluation. The study's goal is to build a decision tree from uncertain data, and existing systems have a number of limitations that need to be investigated further and resolved. Measurement errors, stale data, and repeated measurements all contribute to data uncertainty. There are numerous problems with classification, and this applies across a wide range of data mining applications. Data classification using decision trees is very popular because of their simple and robust structure. The accuracy of the decision tree for the uncertain data used is high because appropriate pdfs have been used. Improve the efficiency of a constructed tree by employing various pruning techniques. In comparison to other techniques, the proposed decision tree for uncertain data achieves higher efficiency. For the construction of the decision tree, this method uses classical algorithms that generate enormous numbers of data tuples (one for each decision). The proposed method achieves a better result because the execution time is shorter, and the system's efficiency is higher. The proposed work will be extended in the future to improve the data classifiers' pruning efficiency when building decision trees. This lays the groundwork for the rest of the research project