PEBL: positive example based learning for Web page classification using SVM

By Hwanjo Yu, Jiawei Han, Kevin Chen-Chuan Chang. Knowledge Discovery and Data Mining, 2002

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f724a3aa8bd82d7a290ee09aaa88f303ee7a013

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. B. Liu
2. Philip S. Yu
3. Qiang Yang
4. Geli Fei
5. Bing Liu
6. Shehroz S. Khan
7. Michael G. Madden
8. Charu C. Aggarwal

In Computer Vision and Pattern Recognition

1. B. Liu
2. Jiawei Han
3. Wee Sun Lee
4. Shehroz S. Khan
5. Michael G. Madden
6. Jiong Yang
7. Qiang Yang
8. Jesse Davis

In Molecular Biology

1. Michael G. Madden
2. Shehroz S. Khan
3. Jessa Bekker
4. Jesse Davis
5. B. Liu
6. Wee Sun Lee
7. Jiawei Han
8. Hwanjo Yu

Citations in the corpus

• Object detection using feature subset selection (2004, neutral): “Speci1c applications include text classi1cation [54], speed recognition [55], gene classi1cation [56], and webpage classi1cation [57].”
• 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).”
• Learning from positive and unlabeled data: a survey (2018, methodology): “S-EM [63] Spy EM NB ∆E Roc-SVM [54] Rocchio Iterative SVM FNR > 5% Roc-Clu-SVM [54] Rocchio∗ Iterative SVM FNR > 5% PEBL [114,113] 1-DNF Iterative SVM Last A-EM [56] Augmented Negatives EM NB ∆F LGN [55] Single Negative BN / PE PUC [115] PE (EM) NB Unspecified WVC/PSOC[81] 1-DNF∗ Iterative SVM Vote ”
• 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).”
• Classifying large data sets using SVMs with hierarchical clusters (2003, methodology): “Support vector machines (SVMs) have been promising methods for data classification and regression [23, 4, 14, 6, 20, 24].”
• A Survey of Recent Trends in One Class Classification (2009, methodology): “For example, in order to construct a homepage classifier [3], collecting sample of homepages”
• Building text classifiers using positive and unlabeled examples (2003, methodology): “These algorithms include SEM [20], PEBL [34], and Roc-SVM [18].”
• PEBL: Web page classification without negative examples (2004, methodology): “To eliminate the need for manually collecting negative training examples in the preprocessing, we proposed a framework, called Positive Example Based Learning (PEBL) [8].”
• Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (2003, methodology): “A similar algorithm using support vector machines (SVM) was proposed in (Yu et al., 2002).”
• 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].”