A Novel Machine Learning Algorithm to Reduce Prediction Error and Accelerate Learning Curve for Very Large Datasets

This paper presents a novel machine learning algorithm with an improved accuracy and a faster learning curve, for very large datasets. Previously, an algorithm using lr-partitions was designed to improve upon C4.5. However, this algorithm has a relatively high percentage of undefined combinations of attribute values in its final results, increasing the learning error. In this paper, a new type of clustering algorithm was proposed to generate output values for those undefined combinations, thus accelerating the learning curve and reducing the prediction error by several percentage points on various popular datasets from the UCI Machine Learning Database.