Earticle

초록

영어

Increasing access to large-scale, high-dimensional and non-stationary streams in many real applications has made it necessary to design new dynamic classification algorithms. Most existing approaches for the textual stream classification are able to train the model relying on labeled data. However, only a limited number of instances can be labeled in a real streaming environment since large-scale data appear at a high speed. Therefore, it is useful to make unlabeled instances available for training and updating the ensemble models. In this paper, we present a new ensemble framework with partial labeled instances for learning from the textual stream. A new semi-supervised cluster-based classifier is proposed as the sub-classifier in our approach. In order to integrate these sub-classifiers, we propose an adaptive selection method. Empirical evaluation of textual streams reveals that our approach outperforms state-of-the-art stream classification algorithms.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Cluster Classifier Ensemble Model with Partial Labeled Instances (CCEM-PL)
 4. Cluster-based Classifiers (CCs) Using Partially Labeled Instances
 5. Selection method and Voting Method
  5.1. Selection Method and Accuracy Weight
  5.2. Voting Strategy
 6. Experiments
  6.1. Datasets
  6.2. Compared Models
  6.3. Results
 Acknowledgements
 References

키워드

저자

• Ge Song [ Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School ]
• Yan Li [ School of Computer Engineering, Shenzhen Polytechnic, China ]
• Chunshan Li [ Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School ]
• Jingjing Chen [ Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China, School of Science and Technology, China Jiliang University, Hangzhou, China ]
• Yunming Ye [ Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605