Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
Mining Relevant Text from Unlabelled Documents
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Classifier Selection for Effective Mining from Noisy Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Investigations on event evolution in TDT
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Semantic similarity methods in wordNet and their application to information retrieval on the web
Proceedings of the 7th annual ACM international workshop on Web information and data management
Text Classification without Negative Examples Revisit
IEEE Transactions on Knowledge and Data Engineering
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Clustering-training for Data Stream Mining
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
An active learning system for mining time-changing data streams
Intelligent Data Analysis
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Learning to Classify Documents with Only a Small Positive Training Set
ECML '07 Proceedings of the 18th European conference on Machine Learning
Active Learning from Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Text classification from unlabeled documents with bootstrapping and feature projection techniques
Information Processing and Management: an International Journal
One-Class Classification of Text Streams with Concept Drift
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Building a Text Classifier by a Keyword and Unlabeled Documents
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Document clustering using synthetic cluster prototypes
Data & Knowledge Engineering
SyMSS: A syntax-based measure for short-text semantic similarity
Data & Knowledge Engineering
Autopedia: automatic domain-independent Wikipedia article generation
Proceedings of the 20th international conference companion on World wide web
Unsupervised extraction of keywords from news archives
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
One-class learning and concept summarization for data streams
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Learning from positive and unlabeled examples with different data distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
Identification of trends from patents using self-organizing maps
Expert Systems with Applications: An International Journal
Analyzing multilingual knowledge innovation in patents
Expert Systems with Applications: An International Journal
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Traditional approaches for text data stream classification usually require the manual labeling of a number of documents, which is an expensive and time consuming process. In this paper, to overcome this limitation, we propose to classify text streams by keywords without labeled documents so as to reduce the burden of labeling manually. We build our base text classifiers with the help of keywords and unlabeled documents to classify text streams, and utilize classifier ensemble algorithms to cope with concept drifting in text data streams. Experimental results demonstrate that the proposed method can build good classifiers by keywords without manual labeling, and when the ensemble based algorithm is used, the concept drift in the streams can be well detected and adapted, which performs better than the single window algorithm.