Classification of Multivariate Time Series and Structured Data Using Constructive Induction

Kadous, Mohammed Waleed; Sammut, Claude

doi:10.1007/s10994-005-5826-5

Cited by 96 publications

(57 citation statements)

References 17 publications

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“…Model based methods are also applied to classify simple time series, such as HMM which is widely used in speech recognition [47]. Multivariate time series classification has been used for gesture recognition [24] and motion recognition [38]. The multivariate data is generated by a set of sensors which measure the movements of objects in different locations and directions.…”

Section: Time Series Datamentioning

confidence: 99%

A brief survey on sequence classification

Xing

Pei

Keogh

2010

SIGKDD Explor. Newsl.

508

266

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Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selection techniques, the dimensionality of potential features may still be very high and the sequential nature of features is difficult to capture. This makes sequence classification a more challenging task than classification on feature vectors. In this paper, we present a brief review of the existing work on sequence classification. We summarize the sequence classification in terms of methodologies and application domains. We also provide a review on several extensions of the sequence classification problem, such as early classification on sequences and semi-supervised learning on sequences.

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Section: Time Series Datamentioning

confidence: 99%

A brief survey on sequence classification

Xing

Pei

Keogh

2010

SIGKDD Explor. Newsl.

508

266

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“…First, it provides a convenient way to include non-temporal attributes, such as some HRV features that are calculated over the full film clip interval or gender of the subjects, into the analysis, which, for instance, Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) methods do not (Kadous and Sammut 2005). Second, contrary to HMM, this method does not require a large amount of training data (Kadous and Sammut 2005). Third, the creation of a template stream in the DTW method for representation of a typical time series corresponding to a given psychological state is not trivial.…”

Section: Data Mining and Extraction Of Featuresmentioning

confidence: 99%

Beyond cognition and affect: sensing the unconscious

Ivonin

Chang

Díaz

et al. 2014

Behaviour & Information Technology

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In the past decade, research on human-computer interaction has embraced psychophysiological user interfaces that enhance awareness of computers about conscious cognitive and affective states of users and increase their adaptive capabilities. Still, human experience is not limited to the levels of cognition and affect but extends further into the realm of universal instincts and innate behaviors that form the collective unconscious. Patterns of instinctual traits shape archetypes that represent images of the unconscious. This study investigated Further analysis suggested that classification performance could be improved up to 70.3 percent in the case of 7 archetypes by using within-subject models.

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“…Multivariate time series data classification methods were studied in [4,5,6,7,8], including On-demand Classifier [4], HMM (Hidden Markov Models) [5], RNN (Recurrent Neural Network), Dynamic Time Warping [5], weighted ensemble classifier [6] and SAX [7]. These methods involve large numbers of parameters and complex preprocessing step that need to be tuned.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Stream Data Classification Using Simple Text Classifiers

Seo

Kang

Lee

et al. 2006

Lecture Notes in Computer Science

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Abstract. We introduce a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes as input a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a simple text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Naïve Bayes Model and SVM, and for unsupervised, we tested Jaccard, TFIDF, Jaro and JaroWinkler. In our experiments, SVM and TFIDF outperformed the other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

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Classification of Multivariate Time Series and Structured Data Using Constructive Induction

Cited by 96 publications

References 17 publications

A brief survey on sequence classification

A brief survey on sequence classification

Beyond cognition and affect: sensing the unconscious

Multivariate Stream Data Classification Using Simple Text Classifiers

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