@inproceedings{edbf998407c64611918bc11f03ce5a9b,
title = "A noise-correction algorithm based on AdaBoost for mislabeled data classification",
abstract = "AdaBoost is one of the most popular algorithm for classification and has been successfully used for text classification, face detection and tracking. However noise sensitivity is regarded as a major disadvantage and previous works show that AdaBoost will be overfitting in dealing with the data sets with noisy data. To improve the noise tolerance of conventional AdaBoost, this paper proposed a Noise-Correction algorithm for Mislabeled Data (NCMD) to find the noisy data and correct it. Further decision stump is selected as the weak learner of the AdaBoost algorithm for classification. Comparison of simulation results between conventional AdaBoost and the method proposed in this paper shows that the proposed algorithm has improved testing accuracy of the data sets with the noisy data.",
keywords = "AdaBoost, Classification, Decision stump, Mislabeled data, Noise processing",
author = "Xiangyang Liu and Yaping Dai and Guosai Yang and Junjie Ma",
note = "Publisher Copyright: {\textcopyright} 2016, Fuji Technology Press. All rights reserved.; 7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016 ; Conference date: 03-11-2016 Through 06-11-2016",
year = "2016",
language = "English",
series = "ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications",
publisher = "Fuji Technology Press",
booktitle = "ISCIIA 2016 - 7th International Symposium on Computational Intelligence and Industrial Applications",
address = "Japan",
}