The new method of the selection of features for the k-NN classifier in the arteriovenous fistula state estimation

Marcin Grochowina, Lucyna Leniowska

DOI: http://dx.doi.org/10.15439/2016F244

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 281–285 (2016)

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Abstract. In this paper the application of a new method of features selection was presented. Its effects were compared with several other methods of features selection. The study were performed using a data set containing samples of the sound signal emitted by the arteriovenous fistula. The aim was to create a solution with multiclass classification based on the k-NN classifier family allowing for effective and credible assessment of the state of arterial-venous fistula.

References

Marcin Grochowina, Lucyna Leniowska and Piotr Dulkiewicz, “Application of Artificial Neural Networks for the Diagnosis of the Condition of the Arterio-venous Fistula on the Basis of Acoustic Signals,” Brain Informatics and Health, Springer, 2014, pp. 400–411.
Marcin Grochowina and Lucyna Leniowska, “Comparison of SVM and k-NN classifiers in the estimation of the state of the arteriovenous fistula problem,” Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on, IEEE, 2015, pp. 249–254.
Zbigniew Suraj, Neamat El Gayar and Pawel Delimata, “A rough set approach to multiple classifier systems,” Fundamenta Informaticae, IOS Press, 2006, pp. 393–406.
Mikkel Grama, Jens Tranholm Olesena, Hans Christian Riisa, Maiuri Selvaratnama and Michalina Urbaniaka, “Stenosis detection algorithm for screening of arteriovenous fistulae,” 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC 2011), Springer, 2011, pp. 241–244.
Fan, Rong-En and Chen, Pai-Hsuen and Lin, Chih-Jen, “Working set selection using second order information for training support vector machines,” The Journal of Machine Learning Research vol.6, JMLR. org, 2005, pp. 1989–1918.
“WEKA documentation,” http://www.cs.waikato.ac.nz/ml/weka/documentation.html
Remco R. Bouckaert, Eibe Frank, Mark Hall, Richard Kirkby, Peter Reutemann, Alex Seewald, David Scuse, “WEKA Manual,” University of Waikato, 2013.
Tadeusz Morzy, “Eksploracja danych - metody i algorytmy,” PWN, 2013.
Dymitr Ruta “Robust Method of Sparse Feature Selection for MultiLabel Classification with Naive Bayes,” Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on, IEEE, 2014, pp. 375–380. http://dx.doi.org/10.15439/2014F502
Zdravevski, Eftim and Lameski, Petre and Kulakov, Andrea and Gjorgjevikj, Dejan “Feature selection and allocation to diverse subsets for multi-label learning problems with large datasets,” Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on, IEEE, 2014, pp. 387–394. http://dx.doi.org/10.15439/2014F500
Daniel T. Larose, “Data mining methods and models,” John Wiley & Sons, Inc, 2006.
Sierra, B., Larrañaga, P., Inza, I. “K Diplomatic Nearest Neighbour: giving equal chance to all existing classes,” Journal of Artificial Intelligence Research, 2000
Dudani, S.A. “The distance-weighted k-nearest neighbor rule,” IEEE Tran-sactions on Systems, Man, and Cybernetics, Vol. SMC-6, No. 4, 1976, pp. 325–327
Fix, E., Hodges Jr., J.L. “Discriminatory analysis — nonparametric discrimination: Consistency properties,” Project 21-49-004, Report No. 4, USAF School of Aviation Medicine, Randolph Field, TX, USA, 1951, pp. 261–279.
Fix, E., Hodges Jr., J.L. “Discriminatory analysis — nonparametric discrimination: Small sample performance,” Project 21-49-004, Report No. 11, USAF School of Aviation Medicine, Randolph Field, TX, USA, 1952, pp. 280–322.
Lichman, M. “UCI Machine Learning Repository,” http://archive.ics.uci.edu/ml Irvine, CA: University of California, School of Information and Computer Science 2013