Incorporating User Feedback into Machine Learning Systems Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9019s7533

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • 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.
License
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Non-Academic Affiliation
Keyword
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Submitted by Shubhomoy Das (dassh@onid.orst.edu) on 2017-06-13T07:23:12ZNo. of bitstreams: 2license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5)DasShubhomoy2017.pdf: 4494622 bytes, checksum: 6f0041c43b2d8dab784cb2981b38fbf3 (MD5)
  • description.provenance : Made available in DSpace on 2017-06-26T22:56:47Z (GMT). No. of bitstreams: 2license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5)DasShubhomoy2017.pdf: 4494622 bytes, checksum: 6f0041c43b2d8dab784cb2981b38fbf3 (MD5) Previous issue date: 2017-05-18
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2017-06-17T23:04:45Z (GMT) No. of bitstreams: 2license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5)DasShubhomoy2017.pdf: 4494622 bytes, checksum: 6f0041c43b2d8dab784cb2981b38fbf3 (MD5)
  • description.provenance : Approved for entry into archive by Steven Van Tuyl(steve.vantuyl@oregonstate.edu) on 2017-06-26T22:56:47Z (GMT) No. of bitstreams: 2license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5)DasShubhomoy2017.pdf: 4494622 bytes, checksum: 6f0041c43b2d8dab784cb2981b38fbf3 (MD5)

Relationships

In Administrative Set:
Last modified: 11/22/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items