Leveraging Machine Learning for Automated Categorization in Construction Management: A Patent Classification Approach

The rapid growth in patent filings and the increasing complexity of construction management projects necessitate efficient methods for data classification and analysis. This paper proposes the use of advanced machine learning techniques, initially designed for patent classification, to address challenges in construction management analytics.The adaptation of supervised classification models, such as Maximum Entropy and Support Vector Machines, to automate the categorization of construction-related data is examined. Feature sets derived from unigrams, bigrams, and tf-idf representations of construction documents, project plans, and historical data are utilized to enhance decision-making and resource allocation. Additionally, dimensionality reduction strategies, including Principal Component Analysis (PCA), are explored to manage high-dimensional construction data and improve predictive modeling. This approach is designed to enhance project forecasting, risk management, and efficiency, offering a scalable solution for data-driven decision-making in construction management.