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
PAC Learning from Positive Statistical Queries
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
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
Document Clustering Using Locality Preserving Indexing
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
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Learning Bayesian classifiers from positive and unlabeled examples
Pattern Recognition Letters
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
Ensemble based positive unlabeled learning for time series classification
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
An integrated framework for human activity classification
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
DTW-D: time series semi-supervised learning from a single example
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A metric learning based approach to evaluate task-specific time series similarity
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
What users care about: a framework for social content alignment
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In many real-world applications of the time series classification problem, not only could the negative training instances be missing, the number of positive instances available for learning may also be rather limited. This has motivated the development of new classification algorithms that can learn from a small set P of labeled seed positive instances augmented with a set U of unlabeled instances (i.e. PU learning algorithms). However, existing PU learning algorithms for time series classification have less than satisfactory performance as they are unable to identify the class boundary between positive and negative instances accurately. In this paper, we propose a novel PU learning algorithm LCLC (Learning from Common Local Clusters) for time series classification. LCLC is designed to effectively identify the ground truths' positive and negative boundaries, resulting in more accurate classifiers than those constructed using existing methods. We have applied LCLC to classify time series data from different application domains; the experimental results demonstrate that LCLC out-performs existing methods significantly.