Author : Ivan S. Mitzev
Affiliation : Mississippi State University
Country : United States
Category : Computer Science & Information Technology
Volume, Issue,
Month, Year
: 6, 1, November, 2016
Abstract
Time-series
classification is widely used approach for classification. Recent development
known as time-series shapelets, based on local patterns from the time-series,
shows potential as highly predictive and accurate method for data mining. On
the other hand, the slow training time remains an acute problem of this method.
In recent years there was a significant improvement of training time
performance, reducing the training time in several orders of magnitude. This
work tries to maintain low training time- in the range from several second to
several minutes for datasets from the popular UCR database, achieving
accuracies up to 20% higher than the fastest known up to date method. The goal
is achieved by training small 2,3-nodes decision trees and combining their
decisions in pattern that uniquely identifies incoming time-series.
Keyword : Data mining, Time-series shapelets, Combining
classifiers
For More Details : https://airccj.org/CSCP/vol6/csit64816.pdf
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