Learning to rank with only positive examples

Mingzhu Zhu, Wei Xiong, Yi Fang Brook Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Search By Multiple Examples (SBME) is a new search paradigm that allows users to specify their information needs as a set of relevant documents rather than as a set of keywords. In this study, we propose a Transductive Positive Unlabeled learning (TPU learning) based framework for SBME. The framework consists of two steps: 1) identifying potential relevant documents for searching space reduction, and 2) adopting TPU learning methods to re-rank the documents in the new searching space. Using MAP and p@k, we evaluate two state-of-the-art PU learning algorithms and the Rocchio classifier (Rc) for document ranking in the proposed framework. We then adopt the idea of ensemble learning to combine Rc with the two state-of-the-art PU learning algorithms respectively. Experiments conducted on a benchmark dataset show that the ensemble learning based methods lead to a significant improvement in effectiveness.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages87-92
Number of pages6
ISBN (Electronic)9781479974153
DOIs
StatePublished - Feb 5 2014
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: Dec 3 2014Dec 6 2014

Publication series

NameProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Other

Other2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
CountryUnited States
CityDetroit
Period12/3/1412/6/14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Human-Computer Interaction

Keywords

  • Information Need Modeling
  • Positive Unlabeled Learning
  • Search by Multiple Examples
  • Transductive Learning

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