TY - JOUR
T1 - Mining best strategy for multi-view classification
AU - Peng, Jing
AU - Aved, Alex J.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - In multi-view classification, the goal is to find a strategy for choosing the most consistent views for a given task. A strategy is a probability distribution over views. A strategy can be considered as advice given to an algorithm. There can be several strategies, each allocating a different probability mass to a view at different times. In this paper, we propose an algorithm for mining these strategies in such a way that its trust in a view for classification comes close to that of the best strategy. As a result, the most consistent views contribute to multi-view classification. Finally, we provide experimental results to demonstrate the effectiveness of the proposed algorithm.
AB - In multi-view classification, the goal is to find a strategy for choosing the most consistent views for a given task. A strategy is a probability distribution over views. A strategy can be considered as advice given to an algorithm. There can be several strategies, each allocating a different probability mass to a view at different times. In this paper, we propose an algorithm for mining these strategies in such a way that its trust in a view for classification comes close to that of the best strategy. As a result, the most consistent views contribute to multi-view classification. Finally, we provide experimental results to demonstrate the effectiveness of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85007524727&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-40973-3_27
DO - 10.1007/978-3-319-40973-3_27
M3 - Article
AN - SCOPUS:85007524727
SN - 0302-9743
VL - 9714 LNCS
SP - 270
EP - 275
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -