Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Temporal Data Mining in Dynamic Feature Spaces
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Improving tweet stream classification by detecting changes in word probability
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Batch-incremental versus instance-incremental learning in dynamic and evolving data
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Efficient data stream classification via probabilistic adaptive windows
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hi-index | 0.00 |
Streams of objects that are associated with one or more labels at the same time appear in many applications. However, stream classification of multi-label data is largely unexplored. Existing approaches try to tackle the problem by transferring traditional single-label stream classification practices to the multi-label domain. Nevertheless, they fail to consider some of the unique properties of the problem such as within and between class imbalance and multiple concept drift. To deal with these challenges, this paper proposes a novel multilabel stream classification approach that employs two windows for each label, one for positive and one for negative examples. Instance-sharing is exploited for space efficiency, while a time-efficient instantiation based on the k-Nearest Neighbor algorithm is also proposed. Finally, a batch-incremental thresholding technique is proposed to further deal with the class imbalance problem. Results of an empirical comparison against two other methods on three real world datasets are in favor of the proposed approach.