MS Defense: Simultaneous Feature Acquisition and Cost Estimation

MS Thesis Defense

Simultaneous Feature Acquisition and Cost Estimation

Zachary Kurtz

11:00am Thursday, 6 December 2012, ITE 325b

This thesis will address classification problems with two sources of cost: the cost of acquiring feature values and the cost of incorrect classifications. In particular, I address problems with feature costs and instance-dependent misclassification costs. Many real-world applications, such as medical diagnosis, contain both feature acquisition costs and instance-dependent misclassification costs. The goal of my research is to minimize the total cost of classifying an unknown instance. This goal is accomplished with a new approach: Simultaneous Feature Acquisition and Cost Estimation (SFACE), which combines feature acquisition methods with a regression algorithm that estimates misclassification costs. The estimated cost values are used to estimate the expected cost reduction for the acquisition of each feature. SFACE is evaluated by comparing the total cost of operation to the cost incurred by existing cost-insensitive, cost-sensitive, and feature acquisition algorithms. The results show that SFACE results in lower total cost for the tested datasets.

Committee: Dr. Marie desJardins (Chair), Dr. Tim Oates and Dr. Michael Grasso

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