Proceedings ArticleDOI
Genetic algorithms and support vector machines for time series classification
Damian Eads,Daniel Hill,Sean M. Davis,Simon Perkins,Junshui Ma,Reid B. Porter,James Theiler +6 more
- Vol. 4787, pp 74-85
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TLDR
This work introduces an algorithm for classifying time series data that employs evolutionary computation for feature extraction, and a support vector machine for the final backend classification.Abstract:
We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final backend classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.read more
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Journal ArticleDOI
Weighted dynamic time warping for time series classification
TL;DR: A novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW that penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers is proposed.
Journal ArticleDOI
Evolutionary tuning of multiple SVM parameters
Frauke Friedrichs,Christian Igel +1 more
TL;DR: It is demonstrated on benchmark datasets that the CMA-ES improves the results achieved by grid search already when applied to few hyperparameters and that tuning of the scaling and the rotation of Gaussian kernel can lead to better results in comparison to standard Gaussian kernels with a single bandwidth parameter.
Journal ArticleDOI
Highly Comparative Feature-Based Time-Series Classification
Ben D. Fulcher,Nick S. Jones +1 more
TL;DR: A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series, allowing the method to perform well on very large data sets containing long time series or time series of different lengths.
Journal ArticleDOI
A novel LS-SVMs hyper-parameter selection based on particle swarm optimization
TL;DR: A novel hyper-parameter selection method for LS-SVMs is presented based on the particle swarm optimization (PSO), which does not need any priori knowledge on the analytic property of the generalization performance measure and can be used to determine multiplehyper-parameters at the same time.
Journal ArticleDOI
The Genetic Kernel Support Vector Machine: Description and Evaluation
Tom Howley,Michael G. Madden +1 more
TL;DR: This paper proposes a classification technique, which it is called the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier.