Russian version English version
Volume 13   Issue 1   Year 2018
Irina A. Borisova, Olga A. Kutnenko

The Problem of Correction Diagnostic Errors in the Target Attribute With the Function of Rival Similarity

Mathematical Biology & Bioinformatics. 2018;13(1):38-49.

doi: 10.17537/2018.13.38.



  1. de Waal T., Pannekoek J., Scholtus S. Handbook of Statistical Data Editing and Imputation. John Wiley and Sons, Inc. Hoboken, New Jersey; 2011. 456 p. doi: 10.1002/9780470904848
  2. Jason W. Osborne. Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data. 1st Edition. SAGE Publication, Inc. Los Angeles; 2013. 296 p.
  3. Luca Greco. Robust Methods for Data Reduction Alessio Farcomeni. Chapman and Hall/CRC; 2015. 297 p.
  4. Teng C.M. A comparison of noise handling techniques. In: Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference. 2001. P. 269-273.
  5. Quinlan J.R. Induction of decision trees. Machine Learning. 1986:81-106. doi: 10.1007/BF00116251
  6. Frenay B., Verleysen M. Classification in the Presence of Label Noise: a Survey. IEEE Transactions on neural networks and learning systems. 2014;25(5):845-869. doi: 10.1109/TNNLS.2013.2292894
  7. Segata N., Blanzieri E. Noise Reduction for Instance-Based Learning with a Local Maximal Margin Approach. Journal of Intelligent Information Systems 35 (October). 2010. doi: 10.1007/s10844-009-0101-z
  8. Massie S., Craw S., Wiratunga N. When Similar Problems Don’t Have Similar Solutions. In: Proceedings of the 7th International Conference on Case-Based Reasoning (ICCBR 07). Springer-Verlag, Berlin, Heidelberg; 2007. P. 92-106.
  9. Son S.-H., Kim J.-Y. Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning. In: Proceedings of the International Conference on Computational Science and Its Applications. 2006. P. 590-599. doi: 10.1007/11751595_63
  10. Delany S.J., Segata N., Mac Namee B. Profiling instances in noise reduction. Knowledge-Based Systems. 2012;31:28-40. doi: 10.1016/j.knosys.2012.01.015
  11. Borisova I.A., Kutnenko O.A. Outliers detection in datasets with misclassified objects. Machine Learning and Data Analysis. 2015;1(11):1632-1641 (in Russ.).
  12. Yang Y., Wu X., X. Zhu. Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources. In: Proceedings of 8th European Conference on Principles and Practice of Knowledge Discovery in Databases. Pisa, Italy; 2004. doi: 10.1007/978-3-540-30116-5_43
  13. Brodley C.E, Friedl M.A. Identifying Mislabeled Training Data. Journal of Artificial Intelligence Research. 1999;11:131-167.
  14. Wilson D.R., Martinez T.R. Reduction Techniques for Instance-Based Learning Algorithms. Machine Learning. 2000;38(3):257-286. doi: 10.1023/A:1007626913721
  15. Jankowski N., Grochowski M. Comparison of Instances Seletion Algorithms I. Algorithms Survey. Artificial Intelligence and Soft Computing. 2004:1-6.
  16. Brighton H., Mellish C. Advances in Instance Selection for Instance-Based Learning Algorithms. Data Mining and Knowledge Discovery. 2002; 6:153-172. doi: 10.1023/A:1014043630878
  17. Aggarwal C.C. Outlier analysis. Data Mining. 2015:237-263. doi: 10.1007/978-3-319-14142-8_8
  18. Zagoruiko N.G., Borisova I.A., Dyubanov V.V., Kutnenko O.A. Methods of recognition based on the function of rival similarity. Pattern Recognition and Image Analysis. 2008;18(1):1-6. doi: 10.1134/S105466180801001X
  19. Zagoruiko N.G. Kognitivnyi analiz dannykh (Cognitive analysis of data). Novosibirsk; 2013. 186 p. (in Russ.).
  20. Zagoruiko N.G. Prikladnye metody analiza dannykh i znanii (Advanced Methods of Data and Knowledge Analysis). Novosibirsk; 1999. 270 p. (in Russ.).
  21. Zagoruiko N.G., Borisova I.A., Kutnenko O.A., Dyubanov V.V. A construction of a compressed description of data using a function of rival similarity. Journal of Applied and Industrial Mathematics. 2013;7(2):275-286. doi: 10.1134/S199047891302018X
  22. Subbotin S.O. The complex of characteristic and criteria of comparison of training. Mathematical Machines and Systems. 2010;1:25-39 (in Russ.).
  23. Breast Cancer Wisconsin (Diagnostic) Data Set. (accessed July 2016).
  24. Pima Indians Diabetes Data Set. (accessed July 2016).
  25. Parkinsons Data Set. (accessed July 2016).
  26. Wilson D.R., Martinez T.R. Reduction Techniques for Instance-Based Learning Algorithms. Machine learning. 2000;38(3):257-286. doi: 10.1023/A:1007626913721
  27. Fukunaga K. Introduction to statistical pattern recognition. Academic Press; 1972.
Table of Contents Original Article
Math. Biol. Bioinf.
doi: 10.17537/2018.13.38
published in Russian

Abstract (rus.)
Abstract (eng.)
Full text (rus., pdf)


  Copyright IMPB RAS © 2005-2024