Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Hierarchical classification of data streams: a systematic literature review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The classification task usually works with flat and batch learners, assuming problems as stationary and without relations between class labels. Nevertheless, several real-world problems do not assume these premises, i.e., data have labels organized hierarchically and are made available in streaming fashion, meaning that their behavior can drift over time. Existing studies on hierarchical classification do not consider data streams as input of their process, and thus, data is assumed as stationary and handled through batch learners. The same can be said about works on streaming data, as the hierarchical classification is overlooked. Studies concerning each area individually are promising, yet, do not tackle their intersection. This study analyzes the main characteristics of the state-of-the-art works on hierarchical classification for streaming data concerning five aspects: (i) problems tackled, (ii) datasets, (iii) algorithms, (iv) evaluation metrics, and (v) research gaps in the area. We performed a systematic literature review of primary studies and retrieved 3,722 papers, of which 42 were identified as relevant and used to answer the aforementioned research questions. We found that the problems handled by hierarchical classification of data streams include mainly classification of images, human activities, texts, and audio; the datasets are mostly created or synthetic data; the algorithms and evaluation metrics are well-known techniques or based on those; and research gaps are related to dynamic context, data complexity, and computational resources constraints. We also provide implications for future research and experiments to consider common characteristics shared amongst hierarchical classification and data stream classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Not applicable.

Code Availability

Not applicable.

Notes

  1. https://www.sciencedirect.com/search/advanced.

  2. https://www.scopus.com/search/.

  3. https://link.springer.com/search.

  4. https://dl.acm.org/advsearch.cfm.

  5. https://ieeexplore.ieee.org/search/advsearch.jsp?expression-builder.

References

  • Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Machine learn 6(1):37–66

    Article  Google Scholar 

  • Alazrai R, Mowafi Y, Lee CG (2015) Anatomical-plane-based representation for human-human interactions analysis. Pattern Recogn 48(8):2346–2363

    Article  Google Scholar 

  • Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17:1

    MathSciNet  Google Scholar 

  • Anderez DO, Appiah K, Lotfi A, Langesiepen C (2017) A hierarchical approach towards activity recognition. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, ACM, pp 269–274

  • Atkins S, Lewin S, Smith H, Engel M, Fretheim A, Volmink J (2008) Conducting a meta-ethnography of qualitative literature: lessons learnt. BMC Med Res Methodol 8(1):21

    Article  Google Scholar 

  • Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, ACM, pp 1–16

  • Barddal JP, Gomes HM, Enembreck F, Pfahringer B, Bifet A (2016) On dynamic feature weighting for feature drifting data streams. In: Joint european conference on machine learning and knowledge discovery in databases, Springer, pp 129–144

  • Barddal JP, Gomes HM, Enembreck F, Pfahringer B (2017) A survey on feature drift adaptation: definition, benchmark, challenges and future directions. J Syst Softw 127:278–294

    Article  Google Scholar 

  • Bifet A, Kirkby R (2009) Data stream mining a practical approach

  • Breiman L (2001) Random forests. Machine learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Burred JJ, Lerch A (2003) A hierarchical approach to automatic musical genre classification. In: Proceedings of the 6th international conference on digital audio effects, Citeseer, pp 8–11

  • Cano A (2018) A survey on graphic processing unit computing for large-scale data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(1):e1232

    Google Scholar 

  • Cao L, Wang Y, Zhang B, Jin Q, Vasilakos AV (2018) Gchar: an efficient group-based context–aware human activity recognition on smartphone. J Parallel Distrib Comput 118:67–80

    Article  Google Scholar 

  • Cerri R, Pappa GL, Carvalho ACP, Freitas AA (2015) An extensive evaluation of decision tree-based hierarchical multilabel classification methods and performance measures. Comput Intell 31(1):1–46

    Article  MathSciNet  Google Scholar 

  • Chakroun I, Haber T, Ashby TJ (2017) Sw-sgd: The sliding window stochastic gradient descent algorithm. Procedia Computer Science 108:2318–2322 https://doi.org/10.1016/j.procs.2017.05.082, http://www.sciencedirect.com/science/article/pii/S1877050917306221, International Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland

  • Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27:1–27:27, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  • Chavez AG, Fontes J, Afonso P, Pfingsthorn M, Birk A (2016) Automated species counting using a hierarchical classification approach with haar cascades and multi-descriptor random forests. In: OCEANS 2016-Shanghai, IEEE, pp 1–6

  • Chen Z, Wu J, Castiglione A, Wu W (2016) Human continuous activity recognition based on energy-efficient schemes considering cloud security technology. Security Commun Net 9(16):3585–3601

    Article  Google Scholar 

  • Chou PH, Wu MJ, Chen KK (2010) Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system. Expert Syst Appl 37(6):4413–4424

    Article  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46

    Article  Google Scholar 

  • del Campo-Ávila J, Ramos-Jiménez G, Gama J, Morales-Bueno R (2008) Improving the performance of an incremental algorithm driven by error margins. Intell Data Analy 12(3):305–318

    Article  Google Scholar 

  • Defiyanti S, Winarko E, Priyanta S (2019) A survey of hierarchical classification algorithms with big-bang approach. In: 2019 5th International Conference on Science and Technology (ICST), IEEE, vol 1, pp 1–6

  • Djorgovski SG, Mahabal A, Donalek C, Graham MJ, Drake AJ, Moghaddam B, Turmon M (2012) Flashes in a star stream: Automated classification of astronomical transient events. arXiv preprint arXiv:12091681

  • Domingos P, Hulten G (2000) Mining high-speed data streams. In: Kdd, vol 2, p 4

  • Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml

  • Dumais S, Chen H (2000) Hierarchical classification of web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pp 256–263

  • D’hondt E, Verberne S, Oostdijk N, Beney J, Koster C, Boves L, (2014) Dealing with temporal variation in patent categorization. Inf Retrieval 17(5–6):520–544

  • Fan J, Zhang J, Mei K, Peng J, Gao L (2015) Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection. Pattern Recogn 48(5):1673–1687

    Article  Google Scholar 

  • Freitas A, Carvalho A (2007) A tutorial on hierarchical classification with applications in bioinformatics. In: Research and trends in data mining technologies and applications, IGI Global, pp 175–208

  • Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. ACM SIGMOD Rec 34(2):18–26

    Article  MATH  Google Scholar 

  • Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRC

    Book  MATH  Google Scholar 

  • Gama J, Sebastião R, Rodrigues PP (2013) On evaluating stream learning algorithms. Mach Learn 90(3):317–346

    Article  MathSciNet  MATH  Google Scholar 

  • Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):44

    Article  MATH  Google Scholar 

  • Gomes HM, Read J, Bifet A, Barddal JP, Gama J (2019) Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explorations Newsl 21(2):6–22

    Article  Google Scholar 

  • Gu P, Qs Zhu, Zhang C, Zhuang Z (2009) An adaptive hierarchical model based on fusion of ontology and context. Transac Beijing Instit Technol 10:1

    Google Scholar 

  • Hamooni H, Mueen A, Neel A (2016) Phoneme sequence recognition via dtw-based classification. Knowl Inf Syst 48(2):253–275

    Article  Google Scholar 

  • Higgins JP, Green S (2011) Cochrane handbook for systematic reviews of interventions, vol 4. Wiley

    Google Scholar 

  • Huang J, Duan N, Ji P, Ma C, Ding Y, Yu Y, Zhou Q, Sun W et al (2018) A crowdsource-based sensing system for monitoring fine-grained air quality in urban environments. IEEE Internet Things J 6(2):3240–3247

    Article  Google Scholar 

  • Huang KY, Wu CH, Hong QB, Su MH, Chen YH (2019) Speech emotion recognition using deep neural network considering verbal and nonverbal speech sounds. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 5866–5870

  • Jeong S, Lee M (2012) Adaptive object recognition model using incremental feature representation and hierarchical classification. Neural Netw 25:130–140

    Article  MATH  Google Scholar 

  • Kasaei SH, Oliveira M, Lim GH, Lopes LS, Tomé AM (2015) Interactive open-ended learning for 3d object recognition: an approach and experiments. J Intell Robotic Syst 80(3–4):537–553

    Article  Google Scholar 

  • Kauppi JP, Martikainen K, Ruotsalainen U (2010) Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns. Neural Netw 23(10):1226–1237

    Article  Google Scholar 

  • Khowaja SA, Prabono AG, Setiawan F, Yahya BN, Lee SL (2018) Contextual activity based healthcare internet of things, services, and people (hiotsp): an architectural framework for healthcare monitoring using wearable sensors. Comput Netw 145:190–206

    Article  Google Scholar 

  • Kiritchenko S, Famili F (2005) Functional annotation of genes using hierarchical text categorization. Proceedings of BioLink SIG, ISMB

  • Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering

  • Korda AI, Asvestas PA, Matsopoulos GK, Ventouras EM, Smyrnis NP (2015) Automatic identification of oculomotor behavior using pattern recognition techniques. Comput Biol Med 60:151–162

    Article  Google Scholar 

  • Kosmopoulos A, Partalas I, Gaussier E, Paliouras G, Androutsopoulos I (2015) Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Min Knowl Disc 29(3):820–865

    Article  MathSciNet  MATH  Google Scholar 

  • Kotsakis R, Kalliris G, Dimoulas C (2012) Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification. Speech Commun 54(6):743–762

    Article  Google Scholar 

  • Krempl G, Žliobaite I, Brzeziński D, Hüllermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M et al (2014) Open challenges for data stream mining research. ACM SIGKDD Explorations Newsl 16(1):1–10

    Article  Google Scholar 

  • La L, Guo Q, Alonso L, Zhang F (2014) Classifying xml data of semantic sensor networks. Arab J Sci Eng 39(5):3733–3745

    Article  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. biometrics ,1: 159–174

  • Liu J, Wang L, Zhou M, Wang J, Lee S (2018) Fine-grained entity type classification with adaptive context. Soft Comput 22(13):4307–4318

    Article  Google Scholar 

  • Lu Y (1997) Concept hierarchy in data mining: Specification, generation and implementation. PhD thesis, Theses (School of Computing Science)/Simon Fraser University

  • Lughofer E (2010) On-line evolving image classifiers and their application to surface inspection. Image Vis Comput 28(7):1065–1079

    Article  Google Scholar 

  • Martin T, Shen Y, Majidian A (2010) Soft concept hierarchies to summarise data streams and highlight anomalous changes. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, pp 44–54

  • Masud MM, Chen Q, Khan L, Aggarwal C, Gao J, Han J, Thuraisingham B (2010) Addressing concept-evolution in concept-drifting data streams. In: 2010 IEEE International Conference on Data Mining, IEEE, pp 929–934

  • Melo A, Völker J, Paulheim H (2017) Type prediction in noisy rdf knowledge bases using hierarchical multilabel classification with graph and latent features. Int J Artif Intell Tools 26(02):1760011

    Article  Google Scholar 

  • Mermillod M, Bugaiska A, Bonin P (2013) The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects. Front Psychol 4:504. https://doi.org/10.3389/fpsyg.2013.00504, http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00504/abstract

  • Nguyen HL, Woon YK, Ng WK, Wan L (2012) Heterogeneous ensemble for feature drifts in data streams. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, pp 1–12

  • Nguyen HL, Woon YK, Ng WK (2015) A survey on data stream clustering and classification. Knowl Inf Syst 45(3):535–569

    Article  Google Scholar 

  • Noblit GW, Hare RD (1988) Meta-ethnography: Synthesizing qualitative studies, vol 11. sage

  • Parmezan ARS, Souza VM, Batista GE (2018) Towards hierarchical classification of data streams. In: Iberoamerican Congress on Pattern Recognition, Springer, pp 314–322

  • Peixoto R, Cruz C, Silva N (2016) Adaptive learning process for the evolution of ontology-described classification model in big data context. In: 2016 SAI Computing Conference (SAI), IEEE, pp 532–540

  • Peng B, Li J, Chen J, Han X, Xu R, Wong KF (2015) Trending sentiment-topic detection on twitter. In: International Conference on Intelligent Text Processing and Computational Linguistics, Springer, pp 66–77

  • Pereira RM, Costa YM, Silla CN (2021) Handling imbalance in hierarchical classification problems using local classifiers approaches. Data Mining and Knowledge Discovery pp 1–58

  • Pesaranghader A, Viktor H, Paquet E (2018) Reservoir of diverse adaptive learners and stacking fast hoeffding drift detection methods for evolving data streams. Mach Learn 107(11):1711–1743

    Article  MathSciNet  Google Scholar 

  • Protasov S, Khan AM, Sozykin K, Ahmad M (2018) Using deep features for video scene detection and annotation. SIViP 12(5):991–999

    Article  Google Scholar 

  • Puerto-Souza GA, Manivannan S, Trujillo MP, Hoyos JA, Trucco E, Mariottini GL (2015) Enhancing normal-abnormal classification accuracy in colonoscopy videos via temporal consistency. In: Computer-Assisted and Robotic Endoscopy, Springer, pp 129–139

  • Purohit H, Hampton A, Bhatt S, Shalin VL, Sheth AP, Flach JM (2014) Identifying seekers and suppliers in social media communities to support crisis coordination. Comput Supported Coop Work (CSCW) 23(4–6):513–545

    Article  Google Scholar 

  • Quinlan JR (2014) C4. 5: Programs for Machine Learning. Elsevier

  • Quiñonero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND (2009) Dataset shift in machine learning. The MIT Press

    Google Scholar 

  • Ramírez-Gallego S, Krawczyk B, García S, Woźniak M, Herrera F (2017) A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239:39–57

    Article  Google Scholar 

  • Raza M, Awais M, Ellahi W, Aslam N, Nguyen HX, Le-Minh H (2019) Diagnosis and monitoring of alzheimer’s patients using classical and deep learning techniques. Expert Syst Appl

  • Ren Z, Peetz MH, Liang S, Van Dolen W, De Rijke M (2014) Hierarchical multi-label classification of social text streams. In: Proceedings of the 37th International ACM SIGIR Conference on Research & development in Information Retrieval, ACM, pp 213–222

  • Saggese A, Strisciuglio N, Vento M, Petkov N (2019) Learning skeleton representations for human action recognition. Pattern Recogn Lett 118:23–31

    Article  Google Scholar 

  • Shi H, Hamagami T, Xu H, Yu P, Wu Y (2012) A method for classifying packets into network flows based on ghsom. Mobile Netw Appl 17(6):730–739

    Article  Google Scholar 

  • Silla CN, Freitas AA (2011) A survey of hierarchical classification across different application domains. Data Min Knowl Disc 22(1–2):31–72

    Article  MathSciNet  MATH  Google Scholar 

  • Silva-Palacios D, Ferri C, Ramirez-Quintana MJ (2018) Adapting hierarchical multiclass classification to changes in the target concept. In: Conference of the Spanish Association for Artificial Intelligence, Springer, pp 118–127

  • Song Y, Sailer A, Shaikh H (2009) Problem classification method to enhance the itil incident and problem. In: 2009 IFIP/IEEE International Symposium on Integrated Network Management, IEEE, pp 295–298

  • Song Y, Sailer A, Shaikh H (2011) Hierarchical online problem classification for it support services. IEEE Trans Serv Comput 5(3):345–357

    Article  Google Scholar 

  • Sun B, Cao S, He J, Yu L (2018) Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy. Neural Netw 105:36–51

    Article  Google Scholar 

  • Tsymbal A (2004) The problem of concept drift: definitions and related work. Comput Sci Depart Trinity College Dublin 106(2):58

    Google Scholar 

  • Venkatesan R, Er MJ (2014) Multi-label classification method based on extreme learning machines. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, pp 619–624

  • Wang Y, Gong Z, Guo J (2009) Hierarchical classification of business information on the web using incremental learning. In: 2009 IEEE International Conference on e-Business Engineering, IEEE, pp 303–309

  • Weigl E, Heidl W, Lughofer E, Radauer T, Eitzinger C (2016) On improving performance of surface inspection systems by online active learning and flexible classifier updates. Mach Vis Appl 27(1):103–127

    Article  Google Scholar 

  • Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54(1):41–59

    Article  Google Scholar 

  • Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–101

    Article  Google Scholar 

  • Wu F, Zhang J, Honavar V (2005) Learning classifiers using hierarchically structured class taxonomies. In: International Symposium on Abstraction, Reformulation, and Approximation, Springer, pp 313–320

  • Xie L, Fu ZH, Feng W, Luo Y (2011) Pitch-density-based features and an svm binary tree approach for multi-class audio classification in broadcast news. Multimedia Syst 17(2):101–112

    Article  Google Scholar 

  • Yassin NI, Omran S, El Houby EM, Allam H (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Comput Methods Programs Biomed 156:25–45

    Article  Google Scholar 

Download references

Funding

This study was partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. Instituto Federal do Paraná (IFPR) - Campus Pinhais, Pinhais, Brazil

    Eduardo Tieppo

  2. Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Brazil

    Eduardo Tieppo, Roger Robson dos Santos, Jean Paul Barddal & Júlio Cesar Nievola

Authors
  1. Eduardo Tieppo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roger Robson dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean Paul Barddal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Júlio Cesar Nievola
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding author

Correspondence to Eduardo Tieppo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank the Brazilian Research Support Agency Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) for their financial support.

Appendix: Datasets, algorithms and evaluation metrics used in the selected studies and research questions addressed

Appendix: Datasets, algorithms and evaluation metrics used in the selected studies and research questions addressed

See Table 10.

Table 10 Datasets, algorithms and evaluation metrics used in the selected studies and research questions addressed
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tieppo, E., Santos, R.R.d., Barddal, J.P. et al. Hierarchical classification of data streams: a systematic literature review. Artif Intell Rev 55, 3243–3282 (2022). https://doi.org/10.1007/s10462-021-10087-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-021-10087-z

Keywords

Search

Navigation