2012 Poster Sessions : Active Classification Based on Value of Classifier

Student Name : Tianshi Gao
Advisor : Daphne Koller
Research Areas: Artificial Intelligence
Abstract:
Modern classification tasks usually involvemany class labels and can be informed by a broad range of features. Many of these tasks are tackled by constructing a set of classifiers, which are then applied at test time and then pieced together in a fixed procedure determined in advance or at training time. We present an active classification process at the test time, where each classifier in a large ensemble is viewed as a potential observation that might inform our classification process. Observations are then selected dynamically based on previous observations, using a value-theoretic computation that balances an estimate of the expected classification gain from each observation as well as its computational cost. The expected classification gain is computed using a probabilistic model that uses the outcome from previous observations. This active classification process is applied at test time for each individual test instance, resulting in an efficient instance-specific decision path. We demonstrate the benefit of the active scheme on various real-world datasets, and show that it can achieve comparable or even higher classification accuracy at a fraction of the computational costs of traditional methods.

Bio:
Tianshi Gao is a final-year Ph.D. student in the Electrical Engineering Department at Stanford University. He is working with Prof. Daphne Koller in the AI lab of the Computer Science Department to design statistical machine learning algorithms to solve computer vision tasks. He got his Bachelor' Degree with honor from Tsinghua University in 2007 and finished his Master's Degree at Stanford in 2010.