@inproceedings{063df748e3604b97a5995005d8ecbad3,
title = "Learning to rank with only positive examples",
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.",
keywords = "Information Need Modeling, Positive Unlabeled Learning, Search by Multiple Examples, Transductive Learning",
author = "Mingzhu Zhu and Wei Xiong and Wu, {Yi Fang Brook}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 ; Conference date: 03-12-2014 Through 06-12-2014",
year = "2014",
month = feb,
day = "5",
doi = "10.1109/ICMLA.2014.19",
language = "English (US)",
series = "Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "87--92",
editor = "Cesar Ferri and Guangzhi Qu and Xue-wen Chen and Wani, {M. Arif} and Plamen Angelov and Jian-Huang Lai",
booktitle = "Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014",
address = "United States",
}