Support vector machines of interval-based features for time series classification

Rodrı́guez, Juan J.; Alonso, Carlos J.; Maestro, José A.

doi:10.1016/j.knosys.2004.10.007

Cited by 75 publications

(41 citation statements)

References 8 publications

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“…The eight bones in the data are the proximal phalanges of the thumb, little and middle fingers; the middle phalanges of the little and middle fingers; and the distal phalanges of the thumb, middle and little fingers. Each instance of data has a class label that is either infant (0-6 years), junior (7-12 years) or teen (13)(14)(15)(16)(17)(18) years. This approach is similar to the system proposed in [17].…”

Section: New Data Setsmentioning

confidence: 99%

“…Despite the evidence in favour of 1-NN classifiers with Euclidean or Dynamic Time Warping (DTW) distance, there has been a spate of recent research proposing alternative approaches. These include shapelets [13,20,19], weighted dynamic time warping [10], support vector machines built on variable intervals [16], tree based ensembles constructed on summary statistics [6], fusion of alternative distance measures [2], and transform-based ensembles [1]. We consider the shapelet approach one of the most promising of these new methods.…”

Section: Introductionmentioning

confidence: 99%

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A shapelet transform for time series classification

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Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

278

233

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The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.

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Section: New Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A shapelet transform for time series classification

Lines

Davis

Hills

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

278

233

View full text Add to dashboard Cite

show abstract

“…As a result, many alternatives have been proposed. These include: shapelets [14,17,18], weighted DTW [8], support vector machines built on variable intervals [15], tree based ensembles constructed on summary statistics [4], fusion of alternative distance measures [2] and transform-based ensembles [1]. Of these, we feel that shapelets in particular have good potential for TSC due to their interpretability and fast classification of new cases.…”

Section: Introductionmentioning

confidence: 99%

Alternative Quality Measures for Time Series Shapelets

Lines

Bagnall

2012

Lecture Notes in Computer Science

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Abstract. Classification is a very broad and prevalent topic of research within data mining. Whilst heavily related, time series classification (TSC) offers a more specific challenge. One of the most promising approaches proposed for TSC is time series shapelets. In this paper we assess the current quality measure used for shapelet extraction and introduce two statistical tests into the context of shapelet finding. We show that when compared to information gain, these two quality measures can speed up shapelet extraction whilst still producing classifiers that are not statistically significantly different to the original.

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“…The discrete functional samples in each data set are of different lengths (see Table 1). For this reason, all discrete functional variables in a given data set were extended to the same length of the longest one by the method described and used, for example, in Górecki et al (2015) (see also Rodriguez et al, 2005). To obtain the basis functions representation (3) of the observations, the orthonormal Fourier basis and the least squares method of estimating the coefficients were used (see Krzyśko and Waszak, 2013).…”

Section: Computational Experimentsmentioning

confidence: 99%

An Application of Functional Multivariate Regression Model to Multiclass Classification

Krzyśko

Smaga

2017

Statistics in Transition New Series

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In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed method for classification for functional data.

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Support vector machines of interval-based features for time series classification

Cited by 75 publications

References 8 publications

A shapelet transform for time series classification

A shapelet transform for time series classification

Alternative Quality Measures for Time Series Shapelets

An Application of Functional Multivariate Regression Model to Multiclass Classification

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