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Methodology for improving classification accuracy using ontologies: application in the recognition of activities of daily living

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Abstract

Feature construction and selection are two key factors in the field of machine learning (ML). Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. In this paper, we propose a methodology that makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base, expressed in form of ontology. The proposed methodology has been evaluated with three different activities of a popular dataset, showing its effectiveness in the recognition of activities of daily living (ADL). The obtained successful results indicate that the use of extended feature vectors provided by the use of ontologies offers a better accuracy, regarding the original feature vectors of the classic approach, where each feature corresponds to the activation of a sensor. Although the classic approach produces classifiers with accuracies above 92%, the proposed methodology improves those results by 1.9%, on average, without adding more information to the dataset.

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Notes

  1. https://sourceforge.net/p/owlmachinelearning/.

  2. The Weka implementation of the C4.5 classifier is called J48.

  3. http://dl-learner.org.

  4. The application developed makes use of the HermiT OWL Reasoner (http://www.hermit-reasoner.com).

  5. 2\(\times\)Intel Xeon E5 2670, 2.6 GHz with 128 GB of RAM.

References

  • Alemdar H, Ersoy C (2017) Multi-resident activity tracking and recognition in smart environments. J Ambient Intell Hum Comput 8(4):513–529. https://doi.org/10.1007/s12652-016-0440-x

    Article  Google Scholar 

  • Bae IH (2014) An ontology-based approach to adl recognition in smart homes. Future Gener Comput Syst 33:32–41

    Article  Google Scholar 

  • Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  • Böhmann L, Lehmann J, Westphal P (2016) Dl-learner-a framework for inductive learning on the semantic web. Web Semant Sci Serv Agents World Wide Web 39(Supplement C):15–24. https://doi.org/10.1016/j.websem.2016.06.001

    Article  Google Scholar 

  • Brown M, Hua G, Winder S (2011) Discriminative learning of local image descriptors. IEEE Trans Pattern Anal Mach Intell 33(1):43–57

    Article  Google Scholar 

  • Chandrasekaran B, Josephson J, Benjamins V (1999) What are ontologies, and why do we need them? IEEE Intell Syst Appl 14(1):20–26

    Article  Google Scholar 

  • Chen L, Nugent C (2009a) Ontology-based activity recognition in intelligent pervasive environments. Int J Web Inf Syst 5(4):410–430

    Article  Google Scholar 

  • Chen L, Nugent C (2009b) Ontology-based activity recognition in intelligent pervasive environments. Int J Web Inf Syst 5(4):410–430

    Article  Google Scholar 

  • Chen L, Nugent C, Okeyo G (2014) An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans Hum Mach Syst 44(1):92–105. https://doi.org/10.1109/THMS.2013.2293714

    Article  Google Scholar 

  • Cheng W, Kasneci G, Graepel T, Stern D, Herbrich R (2011) Automated feature generation from structured knowledge. In: Proceedings of the 20th ACM international conference on Information and knowledge management, ACM, pp 1395–1404

  • Espinilla M, Nugent C (2017) Computational intelligence for smart environments. Int J Comput Intell Syst 10(1):1250–1251

    Article  Google Scholar 

  • Espinilla M, Medina J, Calzada A, Liu J, Martinez L, Nugent C (2017) Optimizing the configuration of an heterogeneous architecture of sensors for activity recognition, using the extended belief rule-based inference methodology. Microprocess Microsyst 52(Supplement C):381–390. https://doi.org/10.1016/j.micpro.2016.10.007

    Article  Google Scholar 

  • Even S (2011) Graph algorithms. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Fang H, He L, Si H, Liu P, Xie X (2014) Human activity recognition based on feature selection in smart home using back-propagation algorithm. ISA Trans 53(5):1629–1638

    Article  Google Scholar 

  • Ferrández-Pastor FJ, Mora-Mora H, Sánchez-Romero JL, Nieto-Hidalgo M, García-Chamizo JM (2017) Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden markov models. J Ambient Intell Hum Comput 8(4):469–483

    Article  Google Scholar 

  • Gupta P, Dallas T (2014) Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 61(6):1780–1786

    Article  Google Scholar 

  • Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15(6):1437–1447

    Article  Google Scholar 

  • Hopcroft J, Tarjan R (1974) Efficient planarity testing. JACM 21(4):549–568

    Article  MathSciNet  MATH  Google Scholar 

  • Horridge M, Drummond N, Goodwin J, Rector A, Wang HH (2006) The manchester owl syntax. In: Proc. of the 2006 OWL experiences and directions workshop (OWL-ED2006

  • Horrocks I (2008) Ontologies and the semantic web. Commun ACM 51(12):58–67

    Article  Google Scholar 

  • Horrocks I, Patel-Schneider P, Van Harmelen F (2003) From SHIQ and RDF to OWL: the making of a web ontology language. Web Semant 1(1):7–26

    Article  Google Scholar 

  • Kanter JM, Veeramachaneni K (2015) Deep feature synthesis: towards automating data science endeavors. In: Data science and advanced analytics (DSAA), 2015. 36678 2015. IEEE international conference on, IEEE, pp 1–10

  • Knijff J, Frasincar F, Hogenboom F (2013) Domain taxonomy learning from text: The subsumption method versus hierarchical clustering. Data Knowl Eng 83:54–69. https://doi.org/10.1016/j.datak.2012.10.002

    Article  Google Scholar 

  • Kohler J, Philippi S, Specht M, Ruegg A (2006) Ontology based text indexing and querying for the semantic web. Knowl Based Syst 19(8):744–754

    Article  Google Scholar 

  • Korhonen I, Parkka J, Van Gils M (2003) Health monitoring in the home of the future. IEEE Eng Med Biol Mag 22(3):66–73

    Article  Google Scholar 

  • Lehmann J, Auer S, Bëhmann L, Tramp S (2011) Class expression learning for ontology engineering. Web Semant Sci Serv Agents World Wide Web 9(1):71–81. https://doi.org/10.1016/j.websem.2011.01.001

    Article  Google Scholar 

  • Li C, Lin M, Yang L, Ding C (2014) Integrating the enriched feature with machine learning algorithms for human movement and fall detection. J Supercomput 67(3):854–865

    Article  Google Scholar 

  • López Gutiérrez, de la Franca C, Hervás R, Johnson E, Mondéjar T, Bravo J (2017) Extended body-angles algorithm to recognize activities within intelligent environments. J Ambient Intell Hum Comput 8(4):531–549. https://doi.org/10.1007/s12652-017-0463-y

    Article  Google Scholar 

  • Maedche A, Staab S (2001) Ontology learning for the semantic web. IEEE Intell Syst Appl 16(2):72–79

    Article  Google Scholar 

  • Mingers J (1989) An empirical comparison of pruning methods for decision tree induction. Mach Learn 4(2):227–243

    Article  Google Scholar 

  • Motik B, Patel-Schneider PF, Parsia B (2012) Owl 2 web ontology language. structural specification and functional–style syntax (second edition). https://www.w3.org/TR/owl2-syntax/. Accessed 30 Oct 2017

  • Noor MHM, Salcic Z, Kevin I, Wang K (2018) Ontology-based sensor fusion activity recognition. J Ambient Intell Hum Comput 1–15. https://doi.org/10.1007/s12652-017-0668-0

  • Okeyo G, Chen L, Wang H, Sterritt R (2014) Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervas Mob Comput 10:155–172

    Article  Google Scholar 

  • Ordóñez FJ, Roggen D (2016) Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115

    Article  Google Scholar 

  • Ordónez FJ, de Toledo P, Sanchis A (2013) Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5):5460–5477

    Article  Google Scholar 

  • Oukrich N, El Bouazzaoui C, Maach A, Driss E (2017) Human activities recognition based on autoencoder pre-training and back-propagation algorithm. J Theor Appl Inf Technol 95(19):5194–5202

    Google Scholar 

  • Paulheim H (2012) Generating possible interpretations for statistics from linked open data. Research and applications, the semantic web, pp 560–574

  • Quesada FJ, Moya F, Medina J, Martínez L, Nugent C, Espinilla M (2015) Generation of a partitioned dataset with single, interleave and multioccupancy daily living activities, vol 9454. Springer, Cham, pp 60–71

    Google Scholar 

  • Rafferty J, Chen L, Nugent C, Liu J (2015) Goal lifecycles and ontological models for intention based assistive living within smart environments. Comput Syst Sci Eng 30(1):7–18

    Google Scholar 

  • Riboni D, Bettini C (2011) Owl 2 modeling and reasoning with complex human activities. Pervas Mob Comput 7(3):379–395

    Article  Google Scholar 

  • Ristoski P (2015) Towards linked open data enabled data mining. In: European semantic web conference, Springer, pp 772–782

  • Ristoski P, Bizer C, Paulheim H (2015) Mining the web of linked data with rapidminer. Web Semant Sci Serv Agents World Wide Web 35(Part 3):142–151. https://doi.org/10.1016/j.websem.2015.06.004 (semantic Web Challenge 2014)

    Article  Google Scholar 

  • Salguero A, Espinilla M (2017) A flexible text analyzer based on ontologies: an application for detecting discriminatory language. Lang Resour Eval. https://doi.org/10.1007/s10579-017-9387-6

    Article  Google Scholar 

  • Shewell C, Medina-Quero J, Espinilla M, Nugent C, Donnelly M, Wang H (2017) Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor. Int J Commun Syst 30(5):e3223. https://doi.org/10.1002/dac.3223

    Article  Google Scholar 

  • Singh D, Merdivan E, Hanke S, Kropf J, Geist M, Holzinger A (2017) Convolutional and recurrent neural networks for activity recognition in smart environment. In: Towards integrative machine learning and knowledge extraction, Springer, pp 194–205

  • Sirin E, Parsia B, Grau B, Kalyanpur A, Katz Y (2007) Pellet: a practical owl-dl reasoner. Web Semant 5(2):51–53

    Article  Google Scholar 

  • Terziev Y (2016) Feature generation using ontologies during induction of decision trees on linked data. In: ISWC PhD Symposium

  • Uschold M, Gruninger M (1996) Ontologies: principles, methods and applications. Knowl Eng Rev 11(2):93–136

    Article  Google Scholar 

  • van Kasteren TLM et al (2011) Activity recognition for health monitoring elderly using temporal probabilistic models. ASCI

  • Villalonga C, Razzaq MA, Khan WA, Pomares H, Rojas I, Lee S, Banos O (2016) Ontology-based high-level context inference for human behavior identification. Sensors. https://doi.org/10.3390/s16101617

    Article  Google Scholar 

  • Wei T, Lu Y, Chang H, Zhou Q, Bao X (2015) A semantic approach for text clustering using wordnet and lexical chains. Expert Syst Appl 42(4):2264–2275. https://doi.org/10.1016/j.eswa.2014.10.023

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA, Pal CJ (2016) Data Mining: practical machine learning tools and techniques. Morgan Kaufmann, Cambridge

    Google Scholar 

  • Xu C, Zhang X, He J (2016) Human activity recognition based on quantization on feature’s classification capability (preprints)

  • Zhang F, Ma Z, Li W (2015) Storing owl ontologies in object-oriented databases. Knowl Based Syst 76:240–255. https://doi.org/10.1016/j.knosys.2014.12.020

    Article  Google Scholar 

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Acknowledgements

This project has received partial support from the REMIND Project from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 734355 as well as from the Spanish government by research project TIN2015-66524-P.

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Authors and Affiliations

  1. Universidad de Cádiz, Cádiz, Spain

    A. G. Salguero & P. Delatorre

  2. Universidad de Jaén, Jaén, Spain

    J. Medina & M. Espinilla

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  1. A. G. Salguero
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  2. J. Medina
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  3. P. Delatorre
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  4. M. Espinilla
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Correspondence to A. G. Salguero.

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Salguero, A.G., Medina, J., Delatorre, P. et al. Methodology for improving classification accuracy using ontologies: application in the recognition of activities of daily living. J Ambient Intell Human Comput 10, 2125–2142 (2019). https://doi.org/10.1007/s12652-018-0769-4

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