Building text classifiers using positive and unlabeled examples

By B. Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S. Yu. Third IEEE International Conference on Data Mining, 2003

793 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b8ac3bfc5ca6913acfca19cde5d41da18e460deb

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In Artificial Intelligence

Citations in the corpus

Positive-Unlabeled Learning with Non-Negative Risk Estimator (2017, background): “The second (e.g., Lee & Liu, 2003; Liu et al., 2003) regards all U data as N data but they are weighted smaller.”
Editorial: special issue on learning from imbalanced data sets (2004, methodology): “In such cases, iterative techniques such as Expectation Minimization (EM), have been used to assign/estimate the missing-class labels [33][36].”
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels (2017, methodology): “Early approaches to PU learning modified the loss functions via weighted logistic regression (Lee & Liu, 2003) and biased SVM (Liu et al., 2003) to penalize more”
Outlier Analysis (2013, background): “It has been shown in the literature [307], that the second approach is superior.”
One-Class Collaborative Filtering (2008, methodology): “When unlabeled data are available, a strategy to solve one-class classification problems is to use EM-like algorithms to iteratively predict the negative examples and learn the classifier [28, 17, 26].”
Learning classifiers from only positive and unlabeled data (2008, methodology): “Moreover, the biased SVM method has been reported to do better experimentally [11].”
HRN: A Holistic Approach to One Class Learning (2020, background): “On broader impact, we believe that our holistic one-class learning can help positive and unlabeled (PU) learning [52], open-world learning (or out-of-distribution detection ) [23], and continual learning [11, 63] as all these learning paradigms need to face unseen/novel situations.”
One-class classification: taxonomy of study and review of techniques (2013, neutral): “In this step, Spy-EM (Liu et al ., 2002) uses a Spy technique, PEBL (Yu et al ., 2004) uses a technique called 1-DNF (Yu et al ., 2002), and Roc-SVM (Liu et al ., 2003) uses the Rocchio algorithm (Rocchio, 1971).”
Learning from positive and unlabeled data: a survey (2018, methodology): “The original one is biased SVM which is a standard SVM method that penalizes misclassified positive and negative examples differently [62].”
A Survey of Recent Trends in One Class Classification (2009, methodology): “The last category has recieved much research interest among the text/document classification community [8][9][10] that will be discussed in detail below in Section 3.”
Social Media Text Classification under Negative Covariate Shift (2015, background): “Our work is also related to learning from positive and unlabeled examples, also known as PU learning (Denis, 1998; Yu et al. 2002; Liu et al. 2003; Lee and Liu, 2003; Elkan and Noto, 2008; Li et al. 2010).”