Augmented query strategies for active learning in stream data mining

Mustafa Amir Faisal, Zeyar Aung, Wei Lee Woon, Davor Svetinovic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Active learning is used in situations where the amount of unlabeled data is abundant but it is costly to manually label the data. So, depending on our available budget, from all unlabeled instances we are to select only a subset of them to ask the oracle for manual labeling. Thus, the query strategy, i.e., how relevant instances are selected to be sent to the oracle, plays an important role in active learning. Though active learning is a very established research area, only a few research works have been done on it in the context of stream data mining. Active learning for stream data is more challenging than for static data because the repetition of queries is not feasible as revisiting the data is almost impossible. In this paper, we propose two augmented query strategies for active learning in stream data mining, namely, Margin Sampling with Variable Uncertainty (MSVU) and Entropy Sampling with Uncertainty using Randomization (ESUR). These two strategies are derived and improved from the existing methods of Variable Uncertainty (VU) and Uncertainty using Randomization (UR) respectively. We evaluate the effectiveness of our proposed MSVU and ESUR strategies by comparing them against the original VU and UR on 6 different datasets using two base classifiers: Leveraging Bagging (LB) and Single Classifier Drift (SCD). Experimental results show that our proposed strategies offer promising outcomes for various datasets and detecting concept drift in the data.

Original languageBritish English
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
EditorsChu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
PublisherSpringer Verlag
Pages431-438
Number of pages8
ISBN (Electronic)9783319126425
DOIs
StatePublished - 2014
Event21st International Conference on Neural Information Processing, ICONIP 2014 - Kuching, Malaysia
Duration: 3 Nov 20146 Nov 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Neural Information Processing, ICONIP 2014
Country/TerritoryMalaysia
CityKuching
Period3/11/146/11/14

Keywords

  • Active learning
  • Query strategy
  • Stream data mining

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