Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Temporal Data Mining in Dynamic Feature Spaces
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Proceedings of the 2008 ACM symposium on Applied computing
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
Design and Implementation of a Data Mining System for Malware Detection
Journal of Integrated Design & Process Science
Data stream dynamic clustering supported by Markov chain isomorphisms
Intelligent Data Analysis
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Data stream classification poses many challenges, most of which are not addressed by the state-of-the-art. We present DXMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Data streams are assumed to be infinite in length, which necessitates single-pass incremental learning techniques. Concept-drift occurs in a data stream when the underlying concept changes over time. Most existing data stream classification techniques address only the infinite length and concept-drift problems. However, concept-evolution and feature- evolution are also major challenges, and these are ignored by most of the existing approaches. Concept-evolution occurs in the stream when novel classes arrive, and feature-evolution occurs when new features emerge in the stream. Our previous work addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems. Most of the existing data stream classification techniques, including our previous work, assume that the feature space of the data points in the stream is static. This assumption may be impractical for some type of data, for example text data. DXMiner considers the dynamic nature of the feature space and provides an elegant solution for classification and novel class detection when the feature space is dynamic. We show that our approach outperforms state-of-the-art stream classification techniques in classifying and detecting novel classes in real data streams.