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PhD Final Examination – Shubhomoy Das


Thursday, May 18, 2017 9:00 AM - 11:00 AM

Incorporating User Feedback into Machine Learning Systems
Although machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree of end user feedback. This is especially true when the machine learning system needs to customize itself to the end user’s preferences, such as in a recommender system, an email classifier or an anomaly detector.

This thesis explores two directions in incorporating end user feedback to machine learning systems. First, I introduce an algorithm that incorporates feature feedback in a semi-supervised text-classification setting. Feature feedback goes beyond instance-label feedback by allowing end users to indicate which feature-value combinations are predictive of the class label. In order to incorporate feature feedback in a semi-supervised setting, I develop a Locally Weighted Logistic Regression algorithm that uses a similarity metric combining information from the user’s feature feedback and information based on label diffusion on the unlabeled data.

Second, I explore the use of instance-level feedback to anomaly detection algorithms. Anomaly detectors commonly return a list of the top outliers in the data. Although these outliers are statistically unusual, some are uninteresting to a user as the internal statistical model may not necessarily be aligned with the user’s semantic notion of an anomaly. I present an algorithm that can increase the number of true anomalies presented to the user if a limited amount of instances are labeled as anomalous or nominal.

Major Advisor: Weng-Keen Wong
Committee: Thomas Dietterich
Committee: Prasad Tadepalli
Committee: Alix Gitelman
GCR: John Dilles


Kelley Engineering Center (campus map)
1114
Calvin Hughes
1 541 737 3168
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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