Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Use of random time-intervals (RTIs) generation for biometric verification
Pattern Recognition
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Anytime classification for a pool of instances
Machine Learning
Detecting outliers on arbitrary data streams using anytime approaches
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
MC-tree: Improving Bayesian anytime classification
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Parallel exact time series motif discovery
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
A fast pivot-based indexing algorithm for metric spaces
Pattern Recognition Letters
Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Bulk loading hierarchical mixture models for efficient stream classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Dealing with concept drift and class imbalance in multi-label stream classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Comparing three lower bounding methods for DTW in time series classification
Proceedings of the Third Symposium on Information and Communication Technology
BT*: an advanced algorithm for anytime classification
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains.