Feature Selection and Classification Techniques for Multivariate Time Series

Chakraborty, Basabi

doi:10.1109/icicic.2007.309

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…We chose to annotate the data at 10Hz, a higher rate than our previous study, because we wanted to capture communication cues that cannot be detected with a smaller data frequency, such as glancing at a person to see what they are doing. We used a machine learning technique called feature selection (Chakraborty, 2007;Guyon & Elisseeff, 2003) to automatically extract sequences of events that are predictive of the physical reaching actions. Then, we used these sequence features in a variety of machine learning classifiers (Mitchell, 1997) and found that a decision tree classifier (Fig.2) performs best on the evaluation data set.…”

Section: Learning the Communication Of Intentmentioning

confidence: 99%

Towards Seamless Human-Robot Handovers

Strabala

Lee

Dragan

et al. 2013

JHRI

224

147

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A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure. To that end, we first study how people hand objects to each other in order to understand their coordination process and the signals and cues that they use and observe with their partners. Based on these studies, we propose a coordination structure for human-robot handovers that considers the physical and social-cognitive aspects of the interaction separately. This handover structure describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers: to agree that the handover will happen (and with what object), to establish the timing of the handover, and to decide the configuration at which the handover will occur. We experimentally evaluate human-robot handover behaviors that exploit this structure, and offer design implications for seamless human-robot handover interactions.

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Section: Learning the Communication Of Intentmentioning

confidence: 99%

Towards Seamless Human-Robot Handovers

Strabala

Lee

Dragan

et al. 2013

JHRI

224

147

View full text Add to dashboard Cite

show abstract

“…In [18] it is successfully demonstrated that the classification of feature-based MTS is faster than other methods. In [19] this is confirmed, where preprocessing was used to select relevant features and remove redundant features before classification. However, MTSC is a complex problem, because the predictors can have many features, there can be many relationships between features, and they depend on the time variable.…”

Section: Introductionmentioning

confidence: 88%

Correlation and Symmetrical Uncertainty-Based Feature Selection for Multivariate Time Series Classification

Saikhu¹,

Arifin²,

Fatichah³

2019

IJIES

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Data relating to everyday phenomena can be recorded in the form of time series. Recently, modeling of time-series has become a topic of interest in data mining. Conducting an analysis of multivariate time series effectively is an essential task for decision-making activities in fields such as meteorology, medicine, and finance. Features selection is a key problem in the analysis of multivariate time series. Rainfall prediction, biomedical classification, pattern recognition, sensor network analysis and so on all have different input features. The problem is that these features have interdependencies and time-delay relationships. Currently, research on the selection of input features of these data still depends on whether they are linear or non-linear. In this paper, we propose a new integration strategy between Pearson Correlation and Symmetrical Uncertainty for relevant feature selection based on linear and non-linear relationships for multivariate time-series classification. We evaluated the goodness of fit of feature subsets using merit value. The meteorological data set was used to test the proposed method. The result showed that the method was able to reduce the number of features by 77.9% features and increase their merit value 2.25 times compared to no input features selection.

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“…The choice of appropriate features plays an important role in this approach. A number of techniques has been proposed for feature subset selection by using compact representation of high dimensional time series into one row to facilitate the application of traditional feature selection algorithms like recursive feature elimination (RFE), zero norm optimization and so forth [10,11]. Time series shapelets, characteristic subsequences of the original series, are recently proposed as the features for time series classification [12].…”

Section: Introductionmentioning

confidence: 99%

Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal

Nakano

Chakraborty

2019

MAKE

Self Cite

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Time series classification (TSC) is becoming very important in the area of pattern recognition with the increased availability of time series data in various natural and real life phenomena. TSC is a challenging problem because, due to the attributes being ordered, traditional machine learning algorithms for static data are not quite suitable for processing temporal data. Due to the gradual increase of computing power, a large number of TSC algorithms have been developed recently. In addition to traditional feature-based, model-based or distance-based algorithms, ensemble and deep networks have recently become popular for time series classification. Time series are essentially huge, and classifying raw data is computationally expensive in terms of both processing and storage. Representation techniques for data reduction and ease of visualization are needed for accurate classification. In this work a recurrence plot-based data representation is proposed and time series classification in conjunction with a deep neural network-based classifier has been studied. A simulation experiment with 85 benchmark data sets from UCR repository has been undertaken with several state of the art algorithms for time series classification in addition to our proposed scheme of classification for comparative study. It was found that, among non-ensemble algorithms, the proposed algorithm produces the highest classification accuracy for most of the data sets.

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Feature Selection and Classification Techniques for Multivariate Time Series

Cited by 13 publications

References 21 publications

Towards Seamless Human-Robot Handovers

Towards Seamless Human-Robot Handovers

Correlation and Symmetrical Uncertainty-Based Feature Selection for Multivariate Time Series Classification

Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal

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