Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise
作者： Ronaldo C. Prati , Julián Luengo , Francisco Herrera
DOI: 10.1007/S10115-018-1244-4
关键词:
摘要: The problem of class noisy instances is omnipresent in different classification problems. However, most research focuses on noise handling binary problems and adaptations to multiclass learning. This paper aims contextualize labels the context non-binary problems, including multiclass, multilabel, multitask, multi-instance ordinal data stream classification. Practical considerations for analyzing under these as well trends, open-ended future directions are analyzed. We believe this could help expand practitioners better identify particular aspects challenging scenarios.
springer.com
sci-hub.se 参考文章(150)
Nada Lavrac, Ciril Groselj, Dragan Gamberger, Experiments with Noise Filtering in a Medical Domain international conference on machine learning. pp. 143- 151 ,(1999)
Hsuan-tien Lin, Han-hsing Tu, One-sided Support Vector Regression for Multiclass Cost-sensitive Classification international conference on machine learning. pp. 1095- 1102 ,(2010)
Andrew K. Rider, Reid A. Johnson, Darcy A. Davis, T. Ryan Hoens, Nitesh V. Chawla, Classifier Evaluation with Missing Negative Class Labels Advances in Intelligent Data Analysis XII. pp. 380- 391 ,(2013) , 10.1007/978-3-642-41398-8_33
Dani Yogatama, Gideon Mann, Efficient Transfer Learning Method for Automatic Hyperparameter Tuning international conference on artificial intelligence and statistics. pp. 1077- 1085 ,(2014)
Shahin Jabbari, Robert C. Holte, Sandra Zilles, PAC-Learning with General Class Noise Models Lecture Notes in Computer Science. pp. 73- 84 ,(2012) , 10.1007/978-3-642-33347-7_7
Francisco Javier Girón González-Torre, C.J. Pérez, M. Ruiz, J. Martín, C. Rojano, Misclassified multinomial data: a Bayesian approach Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A: Matemáticas ( RACSAM ). ,vol. 101, pp. 71- 80 ,(2007)
Taghi M. Khoshgoftaar, Yong Shi, Xindong Wu, Xingquan Zhu, An empirical study of the noise impact on cost-sensitive learning international joint conference on artificial intelligence. pp. 1168- 1173 ,(2007)
Clayton Scott, {A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels} international conference on artificial intelligence and statistics. pp. 838- 846 ,(2015)
Salvador García, Julián Luengo, Francisco Herrera, Data Preprocessing in Data Mining Springer Publishing Company, Incorporated. ,(2016) , 10.1007/978-3-319-10247-4
Marcus A. Maloof, Ryszard S. Michalski, Selecting Examples for Partial Memory Learning Machine Learning. ,vol. 41, pp. 27- 52 ,(2000) , 10.1023/A:1007661119649