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Impact of Data Characteristics on Feature Selection Techniques Performance

References 1. ABE, N., KUDO, M., TOYAMA, J., SHIMBO, M. 2006. Classifier-independent feature selection on the basis of divergence criterion. Pattern Analysis & Applications , 9 (2), 2006, pp. 127.-137. 2. CHRYSOSTOMOU, K. 2009. Wrapper Feature Selection. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining , Second Edition, pp. 2103-2108. Hershey, PA: Information Science Reference. doi:10.4018/978-1-60566-010-3.ch322 3. DEVIJVER, P. A., KITTLER, J. 1982. Pattern Recognition: A Statistical

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A New Horizo-Vertical Distributed Feature Selection Approach

Second Hellenic Conference on Artificial Intelligence, 2002, pp. 249-256. 4. Das, K., K. Bhaduri, H. Kargupta. A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-To-Peer Networks. – Knowledge and Information Systems, Vol. 24 , 2010, No 3, pp. 341-367. 5. Sheela, M. A., K. Vijayalakshmi. Partition Based Perturbation for Privacy Preserving Distributed Data Mining. – Cybernetics and Information Technologies, Vol. 17 , 2017, No 2, pp. 44-55. 6. Skillicorn, D. B., S. M. McConnell. Distributed Prediction from

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EEG Feature Selection for BCI Based on Motor Imaginary Task

-related potentials and corticospinal excitability, Clinical Neurophysiology , 112 , 2001, 923-930. [8] Masters T., Practical Neural Networks Recipes in C++, Academic Press Inc, 1993. [9] Peterson D. A., Knight J. N., Kirby M. J., Anderson Ch. W., Thaut M. H., Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface, EURASIP Journal on Applied Signal Processing , 19 , 2005, 3128-3140. [10] Pfurtscheller G., Flotzinger D., Kalcher J., Brain-computer interface - a new communication device for

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A Review of Feature Selection and Its Methods

References 1. Yu, L., H. Liu. Efficient Feature Selection via Analysis of Relevance and Redundancy. – J. Mach. Learn. Res., Vol. 5 , 2004, No Oct, pp. 1205-1224. 2. Gheyas, I. A., L. S. Smith. Feature Subset Selection in Large Dimensionality Domains. – Pattern Recognit., Vol. 43 , January 2010, No 1, pp. 5-13. 3. Yang, Y., J. O. Pedersen. A Comparative Study on Feature Selection in Text Categorization. – In: Proc. of 14th International Conference on Machine Learning, ICML’97, 1997, pp. 412-420. 4. Yan, K., D. Zhang. Feature Selection and

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Feature Selection for Website Fingerprinting

& Telecommunications Association (NCTA) 2003 National Show , pages 8–11, 2003. [41] Subhabrata Sen and Jia Wang. Analyzing peer-to-peer traffic across large networks. IEEE/ACM Transactions on Networking (ToN) , 12(2):219–232, 2004. [42] Junhua Yan and Jasleen Kaur. Feature selection for website fingerprinting. Technical Report 18-001, 2018. [43] Rishab Nithyanand, Xiang Cai, and Rob Johnson. Glove: A bespoke website fingerprinting defense. In Proceedings of the 13th Workshop on Privacy in the Electronic Society , pages 131–134. ACM, 2014. [44] Pierre Geurts

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The Feature Selection Problem in Computer–Assisted Cytology

Intelligent Systems and Computing, Vol. 471, Springer, Cham, pp. 3-14. Roffo, G. (2016). Feature selection library (Matlab toolbox), arXiv: 1607.01327. Ronneberger, O., Fischer, P. and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation, CoRR: abs/1505.04597. Ruifrok, A.C. and Johnston, D.A. (2001). Quantification of histochemical staining by color deconvolution, Analytical and Quantitative Cytology and Histology 23(4): 291-299. Sadanandan, S.K., Ranefall, P., Guyader, S.L. and

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An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

to detect abnormalities. Therefore, analysis of ECG signals using a computer-aided tools, potentially helps physicians to efficiently identify abnormalities [ 4 , 5 ]. The four major stages in a heartbeat abnormalities diagnosis procedure are preprocessing, feature extraction, feature selection, and classification [ 6 ]. Various types of artifacts and noise usually contaminate ECG recordings. In the preprocessing stage, the goals are to decrease such artifacts and noise and to improve the signal for subsequent processing. As an important step, feature

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Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

for feature selection in data mining, International Journal of Computer Science and Information Technologies, vol. 1, no. 5, 2010, pp. 443-448. [5] Ch. Brester, M. Sidorov, E. Semenkin, Acoustic Emotion Recognition: TwoWays of Feature Selection Based on Self-Adaptive Multi-Objective Genetic Algorithm, Proceedings of the International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2014, pp. 851-855. [6] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA

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The Impact of Feature Selection on the Information Held in Bioinformatics Data

REFERENCES [1] H. Liu and R. Setiono, “Chi2: Feature selection and discretization of numeric attributes,” in Proc. IEEE 7th Int. Conf. on Tools with Artificial Intelligence, pp. 338–391, 1995. [2] J.R. Quinlan, C4.5: Programs for Machine Learning . – San Mateo, CA: Morgan Kaufmann Publishers, 1993, p. 302. [3] R.C. Holte. “Very simple classification rules perform well on most commonly used datasets,” Machine Learning , vol. 11, pp. 63–91, 1993. http://dx.doi.org/10.1023/A:1022631118932 [4] I. Kononenko, “Estimating Attributes

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Influence of Feature Selection Methods on Classification Sensitivity Based on the Example of A Study of Polish Voivodship Tourist Attractiveness

Abstract

The purpose of this article is to determine the influence of various methods of selection of diagnostic features on the sensitivity of classification. Three options of feature selection are presented: a parametric feature selection method with a sum (option I), a median of the correlation coefficients matrix column elements (option II) and the method of a reversed matrix (option III). Efficiency of the groupings was verified by the indicators of homogeneity, heterogeneity and the correctness of grouping. In the assessment of group efficiency the approach with the Weber median was used. The undertaken problem was illustrated with a research into the tourist attractiveness of voivodships in Poland in 2011.

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