Open Access. Powered by Scholars. Published by Universities.®
Finance and Financial Management Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
- Publication
Articles 1 - 2 of 2
Full-Text Articles in Finance and Financial Management
Top-Down Construction Cost Estimating Model Using An Artificial Neural Network, Douglas D. Gransberg, H. David Jeong, Ilker Karaca, Brendon Gardner
Top-Down Construction Cost Estimating Model Using An Artificial Neural Network, Douglas D. Gransberg, H. David Jeong, Ilker Karaca, Brendon Gardner
Ilker Karaca
This report contains the information and background on top-down cost estimating using artificial neural networks (ANN)_to enhance the accuracy of MDT early estimates of construction costs. Upon conducting an extensive review of MDT’s budgeting and cost estimating efforts, and following a survey of agency experts on the identification of the most salient project attributes with the dual-objectives of low effort and high accuracy, a rational method for top-down variable selection is proposed. Selected variables were further tested in their explanatory power of construction costs through the application of two cost estimating methodologies—multiple regression and artificial neural network methodologies. Both methods …
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
Jennifer L. Priestley
Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.