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Query-sensitive Distance Measure Selection for Time Series Nearest Neighbor Classification
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2016 (Engelska)Ingår i: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 20, nr 1, 5-27 s.Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Many distance or similarity measures have been proposed for time series similarity search. However, none of these measures is guaranteed to be optimal when used for 1-Nearest Neighbor (NN) classification. In this paper we study the problem of selecting the most appropriate distance measure, given a pool of time series distance measures and a query, so as to perform NN classification of the query. We propose a framework for solving this problem, by identifying, given the query, the distance measure most likely to produce the correct classification result for that query. From this proposed framework, we derive three specific methods, that differ from each other in the way they estimate the probability that a distance measure correctly classifies a query object. In our experiments, our pool of measures consists of Dynamic TimeWarping (DTW), Move-Split-Merge (MSM), and Edit distance with Real Penalty (ERP). Based on experimental evaluation with 45 datasets, the best-performing of the three proposed methods provides the best results in terms of classification error rate, compared to the competitors, which include using the Cross Validation method for selecting the distance measure in each dataset, as well as using a single specific distance measure (DTW, MSM, or ERP) across all datasets.

Ort, förlag, år, upplaga, sidor
2016. Vol. 20, nr 1, 5-27 s.
Nyckelord [en]
Time series, classification, distance measures
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-122873DOI: 10.3233/IDA-150791ISI: 000369379100002OAI: oai:DiVA.org:su-122873DiVA: diva2:868696
Tillgänglig från: 2015-11-11 Skapad: 2015-11-11 Senast uppdaterad: 2016-03-02Bibliografiskt granskad

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Papapetrou, Panagiotis
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Institutionen för data- och systemvetenskap
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Intelligent Data Analysis
Systemvetenskap, informationssystem och informatik

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