Exploiting monotonicity via logistic regression in Bayesian network learning Public Deposited

http://ir.library.oregonstate.edu/concern/technical_reports/j6731495n

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use of constrained logistic regression for estimating conditional probability distribution in Bayesian Networks (BN) by using background knowledge in the form of qualitative monotonicity statements. Such background knowledge is treated as a set of constraints on the parameters of a logistic function during training. Our goal of finding the appropriate BN model is two-fold: (a) we want to exploit any monotonic relationship between random variables that may generally exist as domain knowledge and (b) we want to be able to address the problem of estimating the conditional distribution of a random variable with a large number of parents. We discuss variants of the logistic regression model and present an analysis on the corresponding constraints required to implement monotonicity. More importantly, we outline the problem in some of these variants in terms of the number of parameters and constraints which, in some cases, can grow exponentially with the number of parent variables. To address this problem, we present two variants of the constrained logistic regression model, M[superscipt 2b][subscript CLR] and M³[subscript CLR], in which the number of constraints required to implement monotonicity does not grow exponentially with the number of parents hence providing a practicable method for estimating conditional probabilities with very sparse data.
Resource Type
Date Available
Date Issued
Series
Keyword
Subject
Rights Statement
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2013-01-29T17:31:46Z (GMT). No. of bitstreams: 1 Exploiting monotonicity via logistic regression in Bayesian network learning.pdf: 204110 bytes, checksum: e0e3bcdd8a2f39c34ccaeaa206475160 (MD5) Previous issue date: 2013-01-29
  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2013-01-29T17:28:30Z No. of bitstreams: 1 Exploiting monotonicity via logistic regression in Bayesian network learning.pdf: 204110 bytes, checksum: e0e3bcdd8a2f39c34ccaeaa206475160 (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-01-29T17:31:45Z (GMT) No. of bitstreams: 1 Exploiting monotonicity via logistic regression in Bayesian network learning.pdf: 204110 bytes, checksum: e0e3bcdd8a2f39c34ccaeaa206475160 (MD5)

Relationships

In Administrative Set:
Last modified: 07/18/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items