著者

Nattee Cholwich Khamsemanan Nirattaya Theeramunkong Thanaruk Numao Masayuki

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.26, 2012

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Positive and Unlabeled learning (PU learning) is a machine learning approach that focuses on generating a two-class classification model using only a set of positive examples, and a set of unlabeled examples. Various techniques have been proposed for PU learning. Most of the techniques try to detect a group of reliables negative examples from the given unlabeled examples. Then, a classification model can be incrementally built. In this paper, we propose a new technique for detecting the reliable negative examples based on the density of examples in the search space.