Batch-based active learning: Application to social media data for crisis management

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

http://eprints.bournemouth.ac.uk/29862/

Journal: Expert Systems with Applications

ISSN: 0957-4174

Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media community. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL's performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power.

This data was imported from Scopus:

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

http://eprints.bournemouth.ac.uk/29862/

Journal: Expert Systems with Applications

Volume: 93

Pages: 232-244

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2017.10.026

© 2017 Elsevier Ltd Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media c ommunity. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL's performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power.

This source preferred by Hamid Bouchachia

This data was imported from Web of Science (Lite):

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

http://eprints.bournemouth.ac.uk/29862/

Journal: EXPERT SYSTEMS WITH APPLICATIONS

Volume: 93

Pages: 232-244

eISSN: 1873-6793

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2017.10.026

The data on this page was last updated at 04:45 on January 16, 2018.