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

An Ontological Approach for Recommending a Feature Selection Algorithm

  • Conference paper
  • First Online:
Web Engineering (ICWE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13362))

Included in the following conference series:

Abstract

Feature selection plays an important role in machine learning or data mining problems. Removing irrelevant features increases model accuracy and reduces the computational cost. However, selecting important features is not a simple task as one feature selection algorithm does not perform well on all the datasets that are of interest. This paper tries to address the recommendation of a feature selection algorithm based on dataset characteristics and quality. The research uses three types of dataset characteristics along with data quality metrics. The main contribution of the work is the utilization of Semantic Web techniques to develop a novel system that can aid in robust feature selection algorithm recommendations. The system’s strength lies in assisting users of machine learning algorithms by providing more relevant feature selection algorithms for the dataset using an ontology called Feature Selection algorithm recommendation based on Data Characteristics and Quality (FSDCQ). Results are generated using six different feature selection algorithms and four types of classifiers on ten datasets from UCI repository. Recommendations take the form of “Feature selection algorithm X is recommended for dataset i, as it performed better on dataset j, similar to dataset i in terms of class overlap 0.3, label noise 0.2, completeness 0.9, conciseness 0.8 units". While the domain-specific ontology FSDCQ was created to aid in the task of algorithm recommendation for feature selection, it is easily applicable to other meta-learning scenarios.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.w3.org/TR/vocab-dqv/.

  2. 2.

    https://github.com/aparnanayakn/onto-DCQ-FS.git.

References

  1. Albertoni, R., Isaac, A.: Introducing the data quality vocabulary (DQV). Semantic Web 12(1), 81–97 (2021)

    Article  Google Scholar 

  2. Almeida, R., Maio, P., Oliveira, P., Barroso, J.: An ontology-based methodology for reusing data cleaning knowledge. In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD 2015), pp. 202–211. SciTePress (2015)

    Google Scholar 

  3. Bozic, B., Brennan, R., Feeney, K., Mendel-Gleason, G.: Describing reasoning results with RVO, the reasoning violations ontology. In: MEPDaW and LDQ Co-located with ESWC, CEUR Workshop Proceedings, vol. 1585, pp. 62–69 (2016)

    Google Scholar 

  4. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  5. Chen, J., Li, K., Rong, H., Bilal, K., Yang, N., Li, K.: A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Inf. Sci. 435, 124–149 (2018)

    Article  Google Scholar 

  6. Chen, R.C., Huang, Y.H., Bau, C.T., Chen, S.M.: A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Syst. Appl. 39(4), 3995–4006 (2012)

    Article  Google Scholar 

  7. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)

    Article  Google Scholar 

  8. Fernández-López, M., Gómez-Pérez, A., Juristo, N.: Methontology: from ontological art towards ontological engineering (1997)

    Google Scholar 

  9. Fürber, C., Hepp, M.: Towards a vocabulary for data quality management in semantic web architectures. In: Proceedings of the 2011 EDBT/ICDT Workshop on Linked Web Data Management, pp. 1–8. ACM (2011)

    Google Scholar 

  10. Junior, A.C., Debruyne, C., Longo, L., O’Sullivan, D.: On the mental workload assessment of uplift mapping representations in linked data. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2018. CCIS, vol. 1012, pp. 160–179. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14273-5_10

  11. Kalousis, A., Hilario, M.: Feature selection for meta-learning. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 222–233. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45357-1_26

  12. Keet, C.M., Lawrynowicz, A., d’Amato, C., Kalousis, A., Nguyen, P., Palma, R., Stevens, R., Hilario, M.: The data mining optimization ontology. J. Web Semant. 32, 43–53 (2015)

    Article  Google Scholar 

  13. Longo, L., Goebel, R., Lecue, F., Kieseberg, P., Holzinger, A.: Explainable artificial intelligence: concepts, applications, research challenges and visions. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2020. LNCS, vol. 12279, pp. 1–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_1

  14. Mantovani, R.G., Rossi, A.L.D., Alcobaça, E., Vanschoren, J., de Carvalho, A.C.P.L.F.: A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers. Inf. Sci. 501, 193–221 (2019)

    Google Scholar 

  15. Nakamura, M., Otsuka, A., Kimura, H.: Automatic selection of classification algorithms for non-experts using meta-features. China-USA Bus. Rev. 13(3) (2014)

    Google Scholar 

  16. Nayak, A., Bozic, B., Longo, L.: Extending r2rml-f to support dynamic datatype and language tags. Proc. Comput. Sci. 192, 709–716 (2021). Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021

    Google Scholar 

  17. Obeid, C., Lahoud, I., El Khoury, H., Champin, P.A.: Ontology-based recommender system in higher education. In: Companion Proceedings of the The Web Conference 2018, pp. 1031–1034 (2018)

    Google Scholar 

  18. O’Connor, M.J., Halaschek-Wiener, C., Musen, M.A.: Mapping master: a flexible approach for mapping spreadsheets to OWL. In: Patel-Schneider, P.F., et al. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 194–208. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17749-1_13

  19. Oreski, D., Oreski, S., Klicek, B.: Effects of dataset characteristics on the performance of feature selection techniques. Appl. Soft Comput. 52, 109–119 (2017)

    Article  Google Scholar 

  20. Panov, P., Dzeroski, S., Soldatova, L.N.: Ontodm: An ontology of data mining. In: Workshops Proceedings of the 8th IEEE International Conference on Data Mining, pp. 752–760. IEEE Computer Society (2008)

    Google Scholar 

  21. Panov, P., Soldatova, L., Džeroski, S.: OntoDM-KDD: ontology for representing the knowledge discovery process. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds.) DS 2013. LNCS (LNAI), vol. 8140, pp. 126–140. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40897-7_9

    Chapter  Google Scholar 

  22. Panov, P., Soldatova, L.N., Dzeroski, S.: Generic ontology of datatypes. Inf. Sci. 329, 900–920 (2016)

    Article  Google Scholar 

  23. Parmezan, A.R.S., Lee, H.D., Spolaôr, N., Wu, F.C.: Automatic recommendation of feature selection algorithms based on dataset characteristics. Expert Syst. Appl. 185, 115589 (2021)

    Article  Google Scholar 

  24. Parmezan, A.R.S., Lee, H.D., Wu, F.C.: Metalearning for choosing feature selection algorithms in data mining: proposal of a new framework. Expert Syst. Appl. 75, 1–24 (2017)

    Article  Google Scholar 

  25. Peng, Y., Flach, P.A., Soares, C., Brazdil, P.: Improved dataset characterisation for meta-learning. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 141–152. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36182-0_14

    Chapter  Google Scholar 

  26. Pise, N., Kulkarni, P.: Algorithm selection for classification problems. In: SAI Computing Conference (SAI), pp. 203–211. IEEE (2016)

    Google Scholar 

  27. Reif, M., Shafait, F., Dengel, A.: Prediction of classifier training time including parameter optimization. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS (LNAI), vol. 7006, pp. 260–271. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24455-1_25

    Chapter  Google Scholar 

  28. Reif, M., Shafait, F., Goldstein, M., Breuel, T., Dengel, A.: Automatic classifier selection for non-experts. Pattern Anal. Appl. 17(1), 83–96 (2012). https://doi.org/10.1007/s10044-012-0280-z

    Article  MathSciNet  Google Scholar 

  29. Rivolli, A., Garcia, L.P., Soares, C., Vanschoren, J., de Carvalho, A.C.: Meta-features for meta-learning. Knowl. Based Syst. 240, 108101 (2022)

    Article  Google Scholar 

  30. Rodriguez-Muro, M., Rezk, M.: Efficient sparql-to-sql with r2rml mappings. J. Web Semant. 33, 141–169 (2015)

    Article  Google Scholar 

  31. Rosa, R.L., Schwartz, G.M., Ruggiero, W.V., Rodríguez, D.Z.: A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Trans. Indust. Inf. 15(4), 2124–2135 (2018)

    Article  Google Scholar 

  32. Shilbayeh, S., Vadera, S.: Feature selection in meta learning framework. In: Science and Information Conference, pp. 269–275. IEEE (2014)

    Google Scholar 

  33. Song, Q., Wang, G., Wang, C.: Automatic recommendation of classification algorithms based on dataset characteristics. Pattern Recogn. 45(7), 2672–2689 (2012)

    Article  Google Scholar 

  34. Tianxing, M., Myint, M., Guan, W., Zhukova, N., Mustafin, N.: A hierarchical data mining process ontology. In: 28th Conference of Open Innovations Association (FRUCT), pp. 465–471. IEEE (2021)

    Google Scholar 

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

    Article  Google Scholar 

  36. Vanschoren, J.: Meta-learning: A Survey. arXiv preprint arXiv:1810.03548 (2018)

  37. Vanschoren, J., Soldatova, L.: Exposé: an ontology for data mining experiments. In: International Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery (SoKD-2010), pp. 31–46 (2010)

    Google Scholar 

  38. Vilalta, R., Giraud-Carrier, C.G., Brazdil, P., Soares, C.: Using meta-learning to support data mining. Int. J. Comput. Sci. Appl. 1(1), 31–45 (2004)

    MATH  Google Scholar 

  39. Vilone, G., Longo, L.: Classification of explainable artificial intelligence methods through their output formats. Mach. Learn. Knowl. Extract. 3(3), 615–661 (2021)

    Article  Google Scholar 

  40. Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf. Fusion 76, 89–106 (2021)

    Article  Google Scholar 

  41. Wang, G., Song, Q., Sun, H., Zhang, X., Xu, B., Zhou, Y.: A feature subset selection algorithm automatic recommendation method. J. Artif. Intell. Res. 47, 1–34 (2013)

    Article  Google Scholar 

  42. Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semantic Web 7(1), 63–93 (2016)

    Article  Google Scholar 

  43. Zhongguo, Y., Hongqi, L., Ali, S., Yile, A.: Choosing classification algorithms and its optimum parameters based on data set characteristics. J. Comput. 28(5), 26–38 (2017)

    Google Scholar 

Download references

Acknowledgements

This publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant number 18/CRT/6183. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Author information

Authors and Affiliations

  1. SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland

    Aparna Nayak, Bojan Božić & Luca Longo

Authors
  1. Aparna Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bojan Božić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luca Longo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aparna Nayak .

Editor information

Editors and Affiliations

  1. Polytechnic University of Bari, Bari, Italy

    Tommaso Di Noia

  2. Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)

    In-Young Ko

  3. Johannes Kepler University Linz, Linz, Austria

    Markus Schedl

  4. Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nayak, A., Božić, B., Longo, L. (2022). An Ontological Approach for Recommending a Feature Selection Algorithm. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09917-5_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09916-8

  • Online ISBN: 978-3-031-09917-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation