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Early Random Shapelet Forest
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2016 (engelsk)Inngår i: Discovery Science: 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings / [ed] Toon Calders, Michelangelo Ceci, Donato Malerba, Springer, 2016, 261-276 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Early classification of time series has emerged as an increasingly important and challenging problem within signal processing, especially in domains where timely decisions are critical, such as medical diagnosis in health-care. Shapelets, i.e., discriminative sub-sequences, have been proposed for time series classification as a means to capture local and phase independent information. Recently, forests of randomized shapelet trees have been shown to produce state-of-the-art predictive performance at a low computational cost. In this work, they are extended to allow for early classification of time series. An extensive empirical investigation is presented, showing that the proposed algorithm is superior to alternative state-of-the-art approaches, in case predictive performance is considered to be more important than earliness. The algorithm allows for tuning the trade-off between accuracy and earliness, thereby supporting the generation of early classifiers that can be dynamically adapted to specific needs at low computational cost.

sted, utgiver, år, opplag, sider
Springer, 2016. 261-276 s.
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9956
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-135411DOI: 10.1007/978-3-319-46307-0_17ISBN: 978-3-319-46306-3 (tryckt)ISBN: 978-3-319-46307-0 (digital)OAI: oai:DiVA.org:su-135411DiVA: diva2:1045195
Konferanse
19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016
Tilgjengelig fra: 2016-11-08 Laget: 2016-11-08 Sist oppdatert: 2017-04-28bibliografisk kontrollert
Inngår i avhandling
1. Order in the random forest
Åpne denne publikasjonen i ny fane eller vindu >>Order in the random forest
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered.

In this thesis, the challenges related to inducing predictive models from data series using a class of algorithms known as random forests are studied for the purpose of efficiently and effectively classifying (i) univariate, (ii) multivariate and (iii) heterogeneous data series either directly in their sequential form or indirectly as transformed to sparse and high-dimensional representations. In the thesis, methods are developed to address the challenges of (a) handling sparse and high-dimensional data, (b) data series classification and (c) early time series classification using random forests. The proposed algorithms are empirically evaluated in large-scale experiments and practically evaluated in the context of detecting adverse drug events.

In the first part of the thesis, it is demonstrated that minor modifications to the random forest algorithm and the use of a random projection technique can improve the effectiveness of random forests when faced with discrete data series projected to sparse and high-dimensional representations. In the second part of the thesis, an algorithm for inducing random forests directly from univariate, multivariate and heterogeneous data series using phase-independent patterns is introduced and shown to be highly effective in terms of both computational and predictive performance. Then, leveraging the notion of phase-independent patterns, the random forest is extended to allow for early classification of time series and is shown to perform favorably when compared to alternatives. The conclusions of the thesis not only reaffirm the empirical effectiveness of random forests for traditional multidimensional data but also indicate that the random forest framework can, with success, be extended to sequential data representations.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 76 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-004
Emneord
Machine learning, random forest, ensemble, time series, data series, sequential data, sparse data, high-dimensional data
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-142052 (URN)978-91-7649-827-9 (ISBN)978-91-7649-828-6 (ISBN)
Disputas
2017-06-08, L30, NOD-huset, Borgarfjordsgatan 12, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Foundation for Strategic Research , IIS11-0053
Tilgjengelig fra: 2017-05-16 Laget: 2017-04-24 Sist oppdatert: 2017-05-15bibliografisk kontrollert

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