Design and Development of an Optimal Decision Tree based Algorithm to Improve Prediction Performance

Abstract

Medical, marketing, finance, electricity, banking, manufacturing, and newlinetelecommunications are just a few of the fields that can benefit from Data Mining. The newlinedata in many practical applications is unreliable, confused, inconsistent, and noisy. newlineUncertainty arises due to a lack of information or facts. Poor information due to newlineuncertainties in the data makes data mining tasks more challenging. In this proposed newlineresearch work, uncertain data analysis is carried out utilizing a variety of data mining models, namely classification decision-making systems that leverage decision tree models to address the aforementioned uncertainties in categorical and numerical data categorization. The research focuses on identifying and removing biases in the results of newlinethese systems. In terms of the prediction of the target value, the classification algorithm newlinegenerates accurate and useful findings, yet it is also capable of generating predictions that newlineare inefficient or incorrect. As a consequence of this, using optimal Decision Trees in newlineorder to improve the prediction performance of the classifier is recommended. The newlineproposed research work calls for the application of Deep Learning to address the problem newlineof uncertainty in datasets, which is then followed by the construction of the Decision Tree newlinemodels for classification purposes. During the process of implementing the optimal newlineDecision Tree, a few problems arise, as finding the ideal splitter node and dealing with uncertainty in the data. The findings of this proposed work provide a framework for uncertainty estimation and newlinehandling that incorporates a number of different methods. After the data preprocessing process, a Decision Tree classifier is used to categorize it using optimization techniques such as parameter optimization and impurity optimization. The effectiveness of the newlinealgorithms is evaluated using a variety of measures, including accuracy, precision, recall, and f1-score. By comparing different options, the best method for dealing with uncertainty can be determined.