TREATMENT LEARNING: IMPLEMENTATION AND APPLICATION

Data mining and machine learning focus on inducing previously unknown, potentially useful, and ultimately understandable information from data. In this master's thesis, we propose a new learning approach called treatment learning. Treatment learning aims at mining a small number of control variables in a large option space that can lead to better system behavior. It addresses two central issues in data mining: (1) the understandability of learnt theories; (2) how can the learnt theories benefit decision making. We design and implement a novel mining algorithm and deliver two treatment learners that are freely downloadable from an online distribution. We describe the implementation details of both learners and compare them through algorithmic performance analysis. We conduct extensive data experiments and case studies to demonstrate the effectiveness of using treatment learner to seek a small number of control variables that constrain the option space to a tight, near-optimal convergence. We compare treatment learning with other learning schemes in the framework of feature subset selection for supervised classification. Our treatment learner selects smaller feature subsets than most other methods with minimal or no loss in classification accuracy. Treatment learner has been successfully ii applied to various research domains through a collaboration with other researchers. By presenting four examples, we show the general paradigms of using it for decision making.