Data Selection for Neural Networks

• Autorzy: Kordos M.

Identyfikatory

DOI

10.4467/20838476SI.16.012.6193

• Języki publikacji: EN

Abstrakty

EN

Several approaches to joined feature and instance selection in neural network leaning are discussed and experimentally evaluated in respect to classification accuracy and dataset compression, considering also their computational complexity. These include various versions of feature and instance selection prior to the network learning, the selection embedded in the neural network and hybrid approaches, including solutions developed by us. The advantages and disadvantages of each approach are discussed and some possible improvements are proposed.

Słowa kluczowe

EN

neural networks; data selection; feature selection; instance selection

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2016
• Tom: Vol. 25
• Strony: 153--164
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Kordos M.

• Department of Computer Science and Automatics University of Bielsko-Biala Willowa 2, 43-309 Bielsko-Biala, Poland

Bibliografia

• [1] Kordos M., Blachnik M., Bialka S., Instance selection in logical rule extraction for regression problems. Lecture Notes in Artificial Intelligence, 2013, 7895, pp. 167–175.
• [2] Blachnik M., Kordos M., Simplifying SVM with weighted LVQ algorithm. Lecture Notes in Computer Science, 2011, 6936, pp. 212–219.
• [3] Liu H., Computational Methods of Feature Selection. Chapman and Hall, 2007.
• [4] Stanczyk U., Jain L.C., Feature Selection for Data and Pattern Recognition. Springer, 2015.
• [5] Uribe C., Isaza C., Expert knowledge-guided feature selection for data-based industrial process monitoring. Rev. Fac. Ing. Univ. Antioquia, 2012, 65, pp. 112–125.
• [6] Kordos M., Cwiok A., A new approach to neural network based stock trading strategy. Lecture Notes in Computer Science, 2011, 6936, pp. 429–436.
• [7] Hofmann M., Klinkenberg R., RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman and Hall/CRC, 2016.
• [8] Sun X., Chan P.K., An analysis of instance selection for neural networks to improve training speed. International Conference on Machine Learning and Applications, 2014, pp. 288–293.
• [9] Garcia S., Derrac J., Cano J.R., Herrera F., Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34, pp. 417–435.
• [10] Olvera-Lapez J.A., Carrasco-Ochoa J.A., Martin J.F., Kittler J., A review of instance selection methods. Artificial Intelligence Review, 2010, 34, pp. 133–143.
• [11] Grochowski M., Jankowski N., Comparison of instance selection algorithms. Lecture Notes in Computer Science, 2004, 3070, pp. 580–585.
• [12] Antonelli M., Ducange P., Marcelloni F., Genetic training instance selection in multiobjective evolutionary fuzzy systems: A coevolutionary approach. IEEE Transactions on Fuzzy Systems, 2012, 20, pp. 276–290.
• [13] Anwar I.M., Salama K.M., Abdelbar A.F., Instance selection with ant colony optimization. Procedia Computer Science, 2015, 53, pp. 248–256.
• [14] Derrac J., Cornelis C., Garcia S., Herrera F., Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Information Sciences, 2012, 186 (73–92).
• [15] Wilson D.R., Martinez T.R., Reduction techniques for instance-based learning algorithms. Machine Learning, 2000, 38, pp. 257–286.
• [16] Blachnik M., Kordos M., Bagging of instance selection algorithms. Lecture Notes in Computer Science, 2014, 8468, pp. 40–51.
• [17] Kordos M., Instance selection optimization for neural network training. Lecture Notes in Artificial Intelligence, 2016, 9692, pp. 610–620.
• [18] Tsaia C.F., Eberleb W., Chu C.Y., Genetic algorithms in feature and instance selection. Knowledge-Based Systems, 2013, 39, pp. 240–247.
• [19] Leray P., Gallinari P., Feature selection with neural networks. Behaviormetrika, 1999, 26, pp. 145–166.
• [20] Rusiecki A., Kordos M., Kaminski T., Gren K., Training neural networks on noisy data. Lecture Notes in Artificial Intelligence, 2014, 8467, pp. 131–142.
• [21] Alcala-Fdez J., et al., Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. http://sci2s.ugr.es/keel/datasets.php, Journal of Multiple-Valued Logic and Soft Computing, 2011, 17, pp. 255–287.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).