A Hybrid Decision Tree-Neural Network (DT-NN) Model for Large-Scale Classification Problems

As the Age of Information has evolved over the last several decades, the demand for technology which stores, analyzes, and utilizes data has increased substantially. Countless industries such as the medical, the retail, and the aircraft rely on this technology to guide their decision making. In the present paper, we propose a hybrid machine learning algorithm consisting of Decision Trees and Neural Networks which can effectively and efficiently classify data of varying volume and variety. The structure of the hybrid algorithm consists of a decision tree where each node of the tree is a neural network trained to classify a specific category of the output using binary classification. The data with which we used to train and test the classification ability of our algorithm is the Federal Aviation Administration's (FAA's) Boeing 737 maintenance dataset which consists of 137,236 unique records each composed of 72 variables. We perform this by classifying the discrepancy, or cause, of the incident into whether or not the incident occurred during scheduled maintenance operations and then further classifying specific details relating to the incident. Our results indicate that our hybrid algorithm is able to effectively classify incidents with high accuracy and precision. Additionally the algorithm is able to identify the most significant inputs regarding a classification allowing for higher performance and greater optimization. This demonstrates the algorithm's applicability in real-world scenarios while also showcasing the benefits of combining decision trees and neural networks as opposed to using them individually.