@inproceedings{e6e2c6089dd8467eb9a66e3379d935bf,
title = "Noise reduction in regression tasks with distance, instance, attribute and density weighting",
abstract = "The idea presented in this paper is to gradually decrease the influence of selected training vectors on the model: if there is a higher probability that a given vector is an outlier, its influence on training the model should be limited. This approach can be used in two ways: in the input space (e.g. with such methods as k-NN for prediction and for instance selection) and in the output space (e.g. while calculating the error of an MLP neural network). The strong point of this gradual influence reduction is that it is not required to set a crisp outlier definition (outliers are difficult to be optimally defined). Moreover, according to the presented experimental results, this approach outperforms other methods while learning the model representation from noisy data.",
keywords = "instance selection, neural networks, noise reduction",
author = "Miroslaw Kordos and Andrzej Rusiecki and Marcin Blachnik",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2nd IEEE International Conference on Cybernetics, CYBCONF 2015 ; Conference date: 24-06-2015 Through 26-06-2015",
year = "2015",
month = aug,
day = "3",
doi = "10.1109/CYBConf.2015.7175909",
language = "English",
series = "Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "73--78",
editor = "Piotr Jedrzejowicz and Nguyen, \{Ngoc Thanh\} and Tzung-Pei Hong and Ireneusz Czarnowski",
booktitle = "Proceedings - 2015 IEEE 2nd International Conference on Cybernetics, CYBCONF 2015",
address = "United States",
}