Feature-based classification of time-series data
Information processing and technology
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Distance-function design and fusion for sequence data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Using multi-scale histograms to answer pattern existence and shape match queries
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Design of multiple classifier systems for time series data
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Embedding time series data for classification
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
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Time series classification has recently been addressed by a wide range of researchers, for its application in variety of fields. Many algorithms have been proposed. Until now, 1-NN (one-nearest-neighbor) using Dynamic Time Warping (DTW) for distance measurement has been proven to be the best technique to produce the maximum accurate result. In this paper we present the idea of bad records that have the tendency to misclassify other records. To evade the misclassification by these bad records, we propose an enhanced 1-NN algorithm for time series classification that properly handles the badness of records without escalating the time complexity of the technique. As a result the accuracy of 1-NN using DTW is further improved. Experimental results show that the error rate can be minimized up to 100% in some cases.