Paper Info
Title
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification.
Abstract
Different real-world applications of multi-label classification often demand different evaluation criteria. We formalize this demand with a general setup, cost-sensitive multi-label classification (CSMLC), which takes the evaluation criteria into account during learning. Nevertheless, most existing algorithms can only focus on optimizing a few specific evaluation criteria, and cannot systematically deal with different ones. In this paper, we propose a novel algorithm, called condensed filter tree (CFT), for optimizing any criteria in CSMLC. CFT is derived from reducing CSMLC to the famous filter tree algorithm for cost-sensitive multi-class classification via constructing the label powerset. We successfully cope with the difficulty of having exponentially many extended-classes within the powerset for representation, training and prediction by carefully designing the tree structure and focusing on the key nodes. Experimental results across many real-world datasets validate that CFT is competitive with special purpose algorithms on special criteria and reaches better performance on general criteria.
Year
Venue
Field
2014
ICML
Data mining,Computer science,Multi-label classification,Artificial intelligence,Tree structure,Machine learning
DocType
Citations 
PageRank 
Conference
12
0.58
References 
Authors
17
2
Name
Order
Citations
PageRank
Chun-Liang Li115216.48
Hsuan-Tien Lin282974.77