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Data Mining and Knowledge Discovery
Erschienen in: Data Mining and Knowledge Discovery 6/2015

01.11.2015

A general framework for never-ending learning from time series streams

verfasst von: Yanping Chen, Yuan Hao, Thanawin Rakthanmanon, Jesin Zakaria, Bing Hu, Eamonn Keogh

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 6/2015

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Abstract

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.
Vorheriger Artikel Weakly supervised nonnegative matrix factorization for user-driven clustering
Nächster Artikel Quadratic regularization projected Barzilai–Borwein method for nonnegative matrix factorization

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Fußnoten
1
For our purposes, a “never-ending” stream may only last for days or hours. The salient point is the contrast with the batch learning algorithms that the vast majority of time series papers consider (Ding et al. 2008).
 
2
Recall from Sect. 3.3 that the Subsequence Processing Module may choose to discard a subsequence rather than pass it to Frequent Pattern Maintenance.
 
3
And for some sexually dimorphic species such as mosquitoes, the sex.
 
4
Dr. John Michael Criley, MD, FACC, MACP is Professor Emeritus at the David Geffen School of Medicine at UCLA.
 
5
Usually the top thirteen coefficients are used for audio analysis. The first coefficient is a normalized energy parameter, which is not used for speech recognition (Mermelstein 1976).
 
Literatur
Achtert E, Bohm C, Kriegel H-P, Kröger P (2005) Online hierarchical clustering in a data warehouse environment data mining. ICDM, pp 10–17
Ambert JD, Hodgman TP, Laurent EJ, Brewer GL, Iliff MJ, Dettmers R (2009) The northeast bird monitoring handbook. American Bird Conservancy, The Plains, VA
Bardeli R, Wolff D, Kurth F, Koch M, Frommolt KH (2010) Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognit Lett 31:1524–1534CrossRef
Barrenetxea G et al (2008) Sensorscope: out-of-the-box environmental monitoring. In: IPSN, San Francisco
Batista G, Keogh E, Mafra-Neto A, Rowton E (2011) Sensors and software to allow computational entomology, an emerging application of data mining. In: KDD, pp 761–764
Berges M, Goldman E, Matthews HS, Soibelman L (2010) Enhancing electricity audits in residential buildings with non-intrusive load monitoring. J Ind Ecol 14(5):844–858CrossRef
Berlin E, Laerhoven K (2012) Detecting leisure activities with dense motif discovery. In: Proceedings of the 2012 intl conference on uniquitous computing, pp 250–259
Borazio M, Laerhoven K (2012) Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies. In: 2nd ACM SIGHIT
Campanharo ASLO, Sirer MI, Malgren RD, Ramos FM, Nunes LAN (2011) Duality between time series and networks. Plos One 6:e23378CrossRef
Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: Proc’ AAAI
Charikar M, Chen K, Farach-Colton M (2002) Finding frequent items in data streams. InL Proceedings of the 29th ICALP international colloquium on automata, languages and programming, pp 693–703
Chen Y, Why A, Batista G, Mafra-Neto A, Keogh E (2014) Flying insect classification with inexpensive sensors. J Insect Behav 27(5):657–677CrossRef
Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 493–498
Cormode G, Hadjieleftheriou M (2010) Methods for finding frequent items in data streams. VLDB J 19(1):3–20CrossRef
Dagan I, Engelson SP (1995) Committee-based sampling for training probabilistic classifiers. In: ICML, vol 95, pp 150–157
Dawson DK, Efford MG (2009) Bird Population Density Estimated from Acoustic Signals. Journal of Applied Ecology. 46(6):1201–1209CrossRef
Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh EJ (2008) Querying and mining of time series data. Experimental comparison of representations and distance measures. PVLDB 1(2):1542–1552
Dodge Y (2003) Oxford dictionary of statistical terms. OUP, Oxford. ISBN 0-19-850994-4MATH
Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. KDD, pp 213–220
Estan C, Varghese G (2003) New directions in traffic measurement and accounting: focusing on the elephants, ignoring the mice. ACM Trans Comput Syst 21(3):270–313CrossRef
Ferreira F, Bota D, Bross A, Mélot C, Vincent J (2001) Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 286(14):1754–1758CrossRef
Fujii A, Tokunaga T, Inui K, Tanaka H (1998) Selective sampling for example-based word sense disambiguation. Comput Linguist 24(4):573–597
Goldberger A et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215–220CrossRef
Gupta S, Reynolds S, Patel SN (2010) ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. In: Proceedings of the conference on ubiquitous computing
Hinman J, Hickey E (2009) Modeling and forecasting short-term electricity load using regression analysis. Working paper, Illinois State University, Normal (US), Fall
Holyoak DT (2001) Nightjars and their allies: the caprimulgiformes. Oxford University Press, Oxford, New York. ISBN 0-19-854987-3
Hu B, Chen Y, Keogh EJ (2013) Time series classification under more realistic assumptions. In: SDM
Jin C, Qian W, Sha C, Yu J, Zhou A (2003) Dynamically maintaining frequent items over a data stream. In: Proceedings of the 12th ACM CIKM international conference on information and knowledge management, pp 287–294
Jin S, Chen Z, Backus E, Sun X, Xiao B (2012) Characterization of EPG waveforms for the tea green leafhopper on tea plants and their correlation with stylet activities. J Insect Physiol 58:1235–1244CrossRef
Karp R, Papadimitriou C, Shenker S (2003) A simple algorithm for finding frequent elements in sets and bags. ACM Trans Database Syst 28:51–55CrossRef
Kolter J, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. J Mach Learn Res 22:1472–1482
Krishnamurthy A, Balakrishnan S, Xu M, Singh A (2012) Efficient active algorithms for hierarchical clustering. arXiv:​1206.​4672
Lines J, Bagnall A, Caiger-Smith P, Anderson S (2011) Classification of household devices by electricity usage profiles. In: IDEAL, pp 403–412
MacLeod J, Greene T, MacKenzie DI, Allen RB (2012) Monitoring widespread and common bird species on New Zealand’s conservation lands: a pilot study. N Z J Ecol 36(3):300–311
Manku G, Motwani R (2002) Approximate frequency counts over data streams. In: International conference on very large databases, pp 346–357
Mermelstein P (1976) Distance measures for speech recognition, psychological and instrumental. In: Chen CH (ed) Pattern recognition and artificial intelligence. Academic Press, New York
Metwally A, Agrawal D, Abbadi AE (2005) Efficient computation of frequent and top-k elements in data streams. In: International conference on database theory
Mitchell L (1981) Time segregated mosquito collections with a CDC miniature light trap. Mosquito News 42:12
Morales L, Arbetman MP, Cameron SA, Aizen MA (2013) Rapid ecological replacement of a native bumble bee by invasive species. Frontiers Ecol Environ 11:529–534CrossRef
Mueen A, Keogh EJ, Young N (2011) Logical-shapelets: an expressive primitive for time series classification. In: KDD, pp 1154–1162
Mueen A, Keogh EJ (2010) Online discovery and maintenance of time series motifs. In: KDD, pp 1089–1098
Nassar S, Sander J, Cheng C (2004) Incremental and effective data summarization for dynamic hierarchical clustering. In: SIGMOD Conference, pp 467–478
Norris JR (1998) Markov chains. Cambridge university press, CambridgeMATH
Robbins CS (1981) Effect of time of day on bird activity. Stud Avian Biol 6:275–286
Roggen D et al (2012) Collecting complex activity data sets in highly rich networked sensor environments. In: Proc’ 7th IEEE INSS, pp 233–240
Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q et al (2012) Searching and mining trillions of time series subsequences under dynamic time warping. Proceedings of the 18th ACM SIGKDD intersnational conference on knowledge discovery and data mining. ACM, New York, pp 262–270
Rowling JK (1997) Harry Potter and the chamber of secrets. Levine Books (scholastics), New York, Read by Stephan Fry, Arthur A
Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill, New York. ISBN 0-07-054484-0
Settles B (2012) Active learning. Morgan & Claypool, San RafaelMATH
Settles B, Craven M, Friedland L (2008) Active learning with real annotation costs. In: Proceedings of the NIPS workshop on cost-sensitive learning
Shrivastava N, Buragohain C, Agrawal D, Suri S (2004) Medians and beyond: new aggregation techniques for sensor networks. In: ACM SenSys
Stikic M, Huynh T, Laerhoven KV, Schiele B (2008) ADL recognition based on the combination of RFID and accelerometer sensing. In: PervasiveHealth, pp 258–263
Tur G, Hakkani-Tür D, Schapire RE (2005) Combining active and semi-supervised learning for spoken language understanding. Speech Commun 45(2):171–186CrossRef
Van Rijsbergen J (1979) Information retrieval, 2nd edn. Butterworths, London
Veeraraghavan A, Chellappa R, Roy-Chowdhury AK (2006) The function space of an activity. In: Computer vision and pattern recognition
Wu Y, Zhou C, Xiao J, Kurths J, Schellnhuber HJ (2010) Evidence for a bimodal distribution in human communication. Proc Natl Acad Sci USA 107:18803–18808CrossRef
Yu H (2005) SVM selective sampling for ranking with application to data retrieval. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, New York
Zhai S, Kristensson PO, Appert C, Anderson TH, Cao X (2012) Foundational issues in touch-surface stroke gesture design-an integrative review. Found Trends Hum Comput Interact 5(2):97–205CrossRef
Metadaten
Titel
A general framework for never-ending learning from time series streams
verfasst von
Yanping Chen
Yuan Hao
Thanawin Rakthanmanon
Jesin Zakaria
Bing Hu
Eamonn Keogh
Publikationsdatum
01.11.2015
Verlag
Springer US
Erschienen in
Data Mining and Knowledge Discovery / Ausgabe 6/2015
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-014-0388-4

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