Robust Prototype-Based Learning on Data Streams

In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 30; no. 5; pp. 978 - 991
Main Authors: Shao, Junming, Huang, Feng, Yang, Qinli, Luo, Guangchun
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Classification
Clustering
concept drift
Data mining
Data models
Data stream
Data transmission
Distributed databases
Ensemble learning
Evolution
Heuristic algorithms
Maintenance engineering
Modelling
Prototypes
Statistical analysis
Synchronism
synchronization
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data or ensemble learning a set of weighted base classifiers, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a proposed P-Tree, which are obtained based on the error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drifts in data streams, PCA and statistical analysis based heuristic approaches have been introduced. To further learn the associations among distributed data streams, the extended P-Tree structure and KNN-style strategy are introduced. We demonstrate that our new data stream classification approach has several attractive benefits: (a) SyncStream is capable of dynamically modeling the evolving concepts from even a small set of prototypes. (b) Owing to synchronization-based constrained clustering and P-Tree, SyncStream supports efficient and effective data representation and maintenance. (c) SyncStream is also tolerant of inappropriate or noisy examples via error-driven representativeness learning. (d) SyncStream allows learning relationship among distributed data streams at the instance level. The experimental results indicate its efficiency and effectiveness.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2772239