Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2018

Abstract

An important feature characteristic of the data streams in many of today's big data applications is the intrinsic uncertainty, which could happen for both item occurrence and attribute value. While this has already posed great challenges for fundamental data mining tasks such as classification, things are made even more complicated by the fact that completely-labeled examples are usually unavailable in such settings, leaving researchers the only option to learn classifiers on partially-labeled examples on uncertain data streams. Furthermore, there will be concept drift on evolving data streams. To address these challenges, this paper therefore focuses on the study of learning from positive and unlabeled examples (PU) on uncertain data streams. To the best of our knowledge, this paper is the first work to address the uncertainty issue in both item occurrence (occurrence level) and attribute value (attribute level) for the problem of PU learning over streaming data. Firstly, we propose an algorithm to classify positive and unlabeled examples with both uncertainties (PUU). The algorithm extracts reliable positive and negative examples by clustering-based method, and then trains the classifier with Weighted Extreme Learning Machine (Weighted ELM). Secondly, we propose an algorithm of PU learning over uncertain data streams (PUUS). It adopts ensemble model and trains base classification by PUU. In order to detect concept drift, PUUS uses cluster set similarities between the current data block and history data block. We propose different update strategies for different concept drift to adapt to evolving uncertain data streams. Experimental results show that PUUS can effectively classify uncertain data streams with just positive and unlabeled data, while achieving in the meantime good performance in detecting and handling concept drifts.

Keywords

uncertain data streams, PU learning, concept drift, ensemble classifier, ClusterELM

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Neurocomputing

Volume

277

First Page

149

Last Page

160

ISSN

0925-2312

Identifier

10.1016/j.neucom.2017.03.094

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.neucom.2017.03.094

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