An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Generalized feature extraction for structural pattern recognition in time-series data
Generalized feature extraction for structural pattern recognition in time-series data
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
Text Classification without Negative Examples Revisit
IEEE Transactions on Knowledge and Data Engineering
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to identify unexpected instances in the test set
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-Supervised Learning
SETRED: self-training with editing
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Positive unlabeled learning for time series classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Many real-world applications in time series classification fall into the class of positive and unlabeled (PU) learning. Furthermore, in many of these applications, not only are the negative examples absent, the positive examples available for learning can also be rather limited. As such, several PU learning algorithms for time series classification have recently been developed to learn from a small set P of labeled seed positive examples augmented with a set U of unlabeled examples. The key to these algorithms is to accurately identify the likely positive and negative examples from U, but it has remained a challenge, especially for those uncertain examples located near the class boundary. This paper presents a novel ensemble based approach that restarts the detection phase several times to probabilistically label these uncertain examples more robustly so that a reliable classifier can be built from the limited positive training examples. Experimental results on time series data from different domains demonstrate that the new method outperforms existing state-of-the art methods significantly.