Leveraging Mutual Information in Algorithm-Assisted Dynamic Feature Selection for Short-term SOx Emissions Forecasting

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Leveraging Mutual Information in Algorithm-Assisted Dynamic Feature 
Selection for Short-term SOx Emissions Forecasting"

By

Mr. Chun Hin LEUNG


Abstract:

Traditional feature selection methods often impose fixed constraints on the 
size of feature subsets, creating a notable gap between academic research and 
industrial applications. To address this issue, this research study 
introduces Algorithm-Assisted Dynamic Feature Selection, referred to as 
AADFS, which synergizes genetic algorithms and simulated annealing with 
mutual information to optimize the trade-off between exploration and 
exploitation. This allows for the dynamic identification of the most relevant 
features without rigid constraints. AADFS has demonstrated improvements in 
prediction accuracy, especially in the short term, and reductions in 
computational complexity across a variety of datasets, ranging from corporate 
emissions data to publicly available datasets, including solar energy 
forecasts, meteorological patterns, electricity consumption, and financial 
market trends. AADFS aims to offer valuable insights for researchers and 
industry professionals seeking to transform theoretical advancements into 
practice.


Date:                   Friday, 22 August 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 5504
                        Lifts 25/26

Chairman:               Prof. Nevin ZHANG

Committee Members:      Prof. Dit-Yan YEUNG (Supervisor)
                        Dr. Jize ZHANG (CIVL)
                        Ir. Ki On NG (CLP Power)