A Combination of Decision Trees and Instance-Based Learning Master’s Scholarly Paper

People are interested in developing a machine learning algorithm that works well in all situations. I proposed and studied a machine learning algorithm that combines two widely used algorithms: decision trees and instance-based learning. I combine these algorithms by using the decision trees to determine the relevant attributes and then running the noise-resistant instancebased learning algorithm with only the attributes used in the decision tree. After using decision tree algorithms and instance-based learning algorithms from WEKA and data from the UCI Machine Learning Repository to test the combined algorithm, I concluded that this combination of the two algorithms did not produce a better algorithm. [3,6,9] My belief for this is that the attributes that one algorithm feels are relevant are likely different from the attributes that the other algorithm feels is relevant.