A Caltech Library Service

Optimizing the C4.5 Decision Tree Algorithm using MSD-Splitting

Rim, Patrick and Liu, Erin (2020) Optimizing the C4.5 Decision Tree Algorithm using MSD-Splitting. International Journal of Advanced Computer Science and Applications, 11 (10). pp. 41-47. ISSN 2156-5570.

[img] PDF - Published Version
Creative Commons Attribution.


Use this Persistent URL to link to this item:


We propose an optimization of Dr. Ross Quin-lan’s C4.5 decision tree algorithm, used for data mining and classification. We will show that by discretizing and binning a data set’s continuous attributes into four groups using our novel technique called MSD-Splitting, we can significantly improve both the algorithm’s accuracy and efficiency, especially when applied to large data sets. We applied both the standard C4.5 algorithm and our optimized C4.5 algorithm to two data sets obtained from UC Irvine’s Machine Learning Repository: Census Income and Heart Disease. In our initial model, we discretized continuous attributes by splitting them into two groups at the point with the minimum expected information requirement, in accordance with the standard C4.5 algorithm. Using five-fold cross-validation, we calculated the average accuracy of our initial model for each data set. Our initial model yielded a 75.72% average accuracy across both data sets. The average execution time of our initial model was 1,541.57 s for the Census Income data set and 50.54 s for the Heart Disease data set. We then optimized our model by applying MSD-Splitting, which discretizes continuous attributes by splitting them into four groups using the mean and the two values one standard deviation away from the mean as split points. The accuracy of our model improved by an average of 5.11%across both data sets, while the average execution time reduced by an average of 96.72% for the larger Census Income data set and 46.38% for the Heart Disease data set.

Item Type:Article
Related URLs:
URLURL TypeDescription
Additional Information:This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited. This research was conducted at California State University, Fullerton. We would like to thank our research supervisor Dr. Shawn X. Wang, professor of computer science at California State University, Fullerton, for providing invaluable guidance and support.
Subject Keywords:C4.5 Algorithm; decision tree; data mining; machine learning; classification
Issue or Number:10
Record Number:CaltechAUTHORS:20210120-143943517
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107601
Deposited By: Tony Diaz
Deposited On:20 Jan 2021 23:07
Last Modified:20 Jan 2021 23:07

Repository Staff Only: item control page