@inproceedings{90f1da7ed13a4344b4e6a7c7d7ff9f14,
title = "Batch mode active learning for networked data with optimal subset selection",
abstract = "Active learning has increasingly become an important paradigm for classification of networked data, where instances are connected with a set of links to form a network. In this paper, we propose a novel batch mode active learning method for networked data (BMALNeT). Our novel active learning method selects the best subset of instances from the unlabeled set based on the correlation matrix that we construct from the dedicated informativeness evaluation of each unlabeled instance. To evaluate the informativeness of each unlabeled instance accurately, we simultaneously exploit content information and the network structure to capture the uncertainty and representativeness of each instance and the disparity between any two instances. Compared with state-of-the-art methods, our experimental results on three realworld datasets demonstrate the effectiveness of our proposed method.",
keywords = "Active learning, Batch mode, Correlation matrix, Optimal subset",
author = "Haihui Xu and Pengpeng Zhao and Sheng, {Victor S.} and Guanfeng Liu and Lei Zhao and Jian Wu and Zhiming Cui",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 16th International Conference on Web-Age Information Management, WAIM 2015 ; Conference date: 08-06-2015 Through 10-06-2015",
year = "2015",
doi = "10.1007/978-3-319-21042-1_8",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "96--108",
editor = "Yizhou Sun and Jian Li",
booktitle = "Web-Age Information Management - 16th International Conference, WAIM 2015, Proceedings",
}