Comparative Analysis of Classification Performance for U.S. College Enrollment Predictive Modeling Using Four Machine Learning Algorithms (Artificial Neural Network, Decision Tree, Support Vector Machine, Logistic Regression)

Every year, the national high school graduation rate is declining and impacting the number of students applying to colleges. Moreover, the majority of students are applying to more than one college. This makes a lot of colleges to be highly competitive in student recruitment for enrollment and thus, the necessity for institutions to anticipate uncertainties related to budgets expected from student enrollment has increased. Hence enrollment management has become a pivotal sector in higher education institutions. Data and analytics are now a crucial part of enhancing enrollment management. Through big data analytics-driven solutions, institutions expect to improve enrollment by identifying students who are most likely to enroll in college. Machine learning can unlock significant value for colleges by allocating resources effectively to improve enrollment and budgeting. Therefore, a machine learning method is a vital tool for analyzing a large amount of data, and predictive analytics using this method has become a high demand in higher education. Yet higher education is still in the early stages of utilizing machine learning for enrollment management. In this study, I applied four machine learning algorithms to seven years of data on 108,798 students, each with 50 associated features, admitted to a 4-year, non-profit university in Midwest urban area to predict students\u27 college enrollment decisions. By treating the question of whether students offered admission will accept it as a binary classification problem, I implemented four machine learning algorithm classifiers and then evaluate the performance of these algorithms using the metrics of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC and PR curves. The results from this study will indicate the best-performed prediction modeling of students’ college enrollment decisions. This research will expand the case and knowledge of utilizing machine learning methods in the higher education sector, focused on the U.S. College enrollment management field. Moreover, it will expand the knowledge of how the machine learning prediction model can be pragmatically used to support institutions in setting up student enrollment management strategies

Intelligent real-time train rescheduling management for railway system

The issue of managing a large and complex railway system with continuous traffic flows and mixed train services in a safe and punctual manner is very important, especially after disruptive events. In the first part of this thesis an analysis method is introduced which allows the visualisation and measurement of the propagation of delays in the railway network. The BRaVE simulator and the University of Birmingham Single Train Simulator (STS) are also introduced and a train running estimation using STS is described. A practical single junction rescheduling problem is then defined and it investigates how different levels of delays and numbers of constraints may affect the performance of algorithms for network-wide rescheduling in terms of quality of solution and computation time. In order to deal with operational dynamics, a methodology using performance-based supervisory control is proposed to provide rescheduling decisions over a wider area through the application of different rescheduling strategies in appropriate sequences. Finally, an architecture for a real-time train rescheduling framework, based on the distributed artificial intelligence system, is designed in order to handle railway traffic in a large-scale network intelligently. A case study based on part of the East Coast Main Line is followed up to demonstrate the effectiveness of adopting supervisory control to provide the rescheduling options in the dynamic situation

New algorithms for learning and pruning oblique decision trees

In this paper, we present methods for learning and pruning oblique decision trees, We propose a new function for evaluating different split rules at each node while growing the decision tree. Unlike the other evaluation functions currently used in literature (which are all based on some notion of purity of a node), this new evaluation function is based on the concept of degree of linear separability, We adopt a correlation-based optimization technique called the Alopex algorithm for finding the split rule that optimizes our evaluation function at each node. The algorithm we present here is applicable only for 2-class problems. Through empirical studies, we demonstrate that our algorithm learns good compact- decision trees. We suggest a representation scheme for oblique decision trees that makes explicit the fact that an oblique decision tree represents each class as a union of convex sets bounded by hyperplanes in the feature space. Using this representation, we present a new pruning technique. Unlike other pruning techniques, which generally replace heuristically selected subtrees of the original tree by leaves, our method can radically restructure the decision tree. Through empirical investigation, we demonstrate the effectiveness of our method