TY - GEN
T1 - Evidential multi-label classification approach to learning from data with imprecise labels
AU - Younes, Zoulficar
AU - Abdallah, Fahed
AU - Denœux, Thierry
PY - 2010
Y1 - 2010
N2 - Multi-label classification problems arise in many real-world applications. Classically, in order to construct a multi-label classifier, we assume the existence of a labeled training set, where each instance is associated with a set of labels, and the task is to output a label set for each unseen instance. However, it is not always possible to have perfectly labeled data. In many problems, there is no ground truth for assigning unambiguously a label set to each instance, and several experts have to be consulted. Due to conflicts and lack of knowledge, labels might be wrongly assigned to some instances. This paper describes an evidence formalism suitable to study multi-label classification problems where the training datasets are imperfectly labelled. Several applications demonstrate the efficiency of our apporach.
AB - Multi-label classification problems arise in many real-world applications. Classically, in order to construct a multi-label classifier, we assume the existence of a labeled training set, where each instance is associated with a set of labels, and the task is to output a label set for each unseen instance. However, it is not always possible to have perfectly labeled data. In many problems, there is no ground truth for assigning unambiguously a label set to each instance, and several experts have to be consulted. Due to conflicts and lack of knowledge, labels might be wrongly assigned to some instances. This paper describes an evidence formalism suitable to study multi-label classification problems where the training datasets are imperfectly labelled. Several applications demonstrate the efficiency of our apporach.
UR - https://www.scopus.com/pages/publications/77954867886
U2 - 10.1007/978-3-642-14049-5_13
DO - 10.1007/978-3-642-14049-5_13
M3 - Conference contribution
AN - SCOPUS:77954867886
SN - 3642140483
SN - 9783642140488
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 128
BT - Computational Intelligence for Knowledge-Based Systems Design - 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Proceedings
T2 - 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010
Y2 - 28 June 2010 through 2 July 2010
ER -