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Time-Series Classification Based on Individualised Error Prediction

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3 Author(s)
Buza, K. ; Inf. Syst. & Machine Learning Lab., Univ. of Hildesheim, Hildesheim, Germany ; Nanopoulos, A. ; Schmidt-Thieme, L.

Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global choice for k (≥ 1) can become suboptimal, because each individual region of a data set may require a different k value. In this paper, we proposed a novel individualized error prediction (IEP) mechanism that considers a range of k-NN classifiers (for different k values) and uses secondary regression models that predict the error of each such classifier. This permits to perform k-NN time-series classification in a more fine grained fashion that adapts to the varying characteristics among different regions by avoiding the restriction of a single value of k. Our experimental evaluation, using a large collection of real timeseries data, indicates that the proposed method is more robust and compares favorably against two examined baselines by resulting in significant reduction in the classification error.

Published in:

Computational Science and Engineering (CSE), 2010 IEEE 13th International Conference on

Date of Conference:

11-13 Dec. 2010

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