Building text classifiers using positive, unlabeled and ‘outdated’ examples

Abstract

Learning from positive and unlabeled examples (PU learning) is a partially supervised classification that is frequently used in Web and text retrieval system. The merit of PU learning is that it can get good performance with less manual work. Motivated by transfer learning, this paper presents a novel method that transfers the 'outdated data' into the process of PU learning. We first propose a way to measure the strength of the features and select the strong features and the weak features according to the strength of the features. Then, we extract the reliable negative examples and the candidate negative examples using the strong and the weak features (Transfer-1DNF). Finally, we construct a classifier called weighted voting iterative support vector machine (SVM) that is made up of several subclassifiers by applying SVM iteratively, and each subclassifier is assigned a weight in each iteration. We conduct the experiments on two datasets: 20 Newsgroups and Reuters-21578, and compare our method with three baseline algorithms: positive example-based learning, weighted voting classifier and SVM. The results show that our proposed method Transfer-1DNF can extract more reliable negative examples with lower error rates, and our classifier outperforms the baseline algorithms.

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

“…Correspondingly, this paper aims to investigate how to employ textual data features for the detection of concept drift of user opinions, which are prominent in realworld online applications and becomes a major challenge to classification accuracy [19,20]. Concept drift detection approaches are typically used in conjunction with a base classifier, such as the NB and LibSVM (SVM) models to increase classification accuracy [20][21][22][23]. Stream classification models, in general, are designed to train classifiers on both historical and current instances in the stream in order to predict the label sets of incoming instances [7].…”

Modeling Conditions Appropriate for Wildfire in South East China – A Machine Learning Approach

“…Such problems require a special case of semi supervised learning called "learning from positive and unlabeled data" or "PU learning" for short. PU learning only requires positive examples and it normally has two steps, (1) to identify reliable negative examples from the unlabeled dataset, and (2) employ a classifier for classification purposes (Liu et al, 2003;Chen, 2009;Han et al, 2016Han et al, , 2018. Various PU learning techniques have been developed such as spy EM (S-EM) (Liu et al, 2002), positive examplebased learning (PEBL) (Yu et al, 2002), one class support vector machine (Manevitz and Yousef, 2002), Roc-SVM , weighted logistic regression (Lee and Liu, 2003), biased SVM (Liu et al, 2003), and bagging SVM (Mordelet and Vert, 2014) to name a few.…”