Oct 29, 2017 · PU Learning mainly consists of two steps [10]. Step 1: Identify the reliable negative samples (RN) from the unlabeled samples (U) according to ...
Aug 9, 2018 · Secondly, our well-trained detector, which is based on the given normal users and predicted spammers, can distinguish between normal users and ...
Nov 14, 2017 · Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, ...
A novel method only relying on normal users to detect spammers is proposed, which is competitive with supervised methods and a well-trained detector can ...
Aug 9, 2018 · Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, ...
Secondly, our well-trained detector, which is based on the given normal users and predicted spammers, can distinguish between normal users and spammers.
Abstract. In social network, people generally tend to share information with others, thus, those who have frequent access to the social network.
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PUD: Social Spammer Detection Based on PU Learning ... Authors: Yuqi Song; Min Gao; Junliang Yu; Wentao Li; Junhao Wen; Qingyu Xiong. List of references.
Jul 12, 2024 · We present two steps: one picks out reliable spammers from unlabeled samples which is imposed on a voting classifier; while the other trains a ...
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To address the problem, we propose a novel method only relying on normal users to detect spammers exactly. We present two steps: one picks out reliable spammers ...
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