[PDF] Performance Measures Fusion for Experimental Comparison of ...
ceur-ws.org › Vol-2350 › paper17
tal comparison of methods for multi-label classification. It is developed for making a general conclusion using a set of user-specified performance measures.
A performance measures fusion approach based on multi criteria decision analysis is proposed that provides rankings of the compared methods for each�...
Performance Measurement. Conference Paper. Performance measures fusion for experimental comparison of methods for multi-label classification. March 2019.
Aug 28, 2019 � The results of the analysis show that for multi-label classification the best performing methods overall are random forests of predictive�...
Performance measures fusion for experimental comparison of methods for multi-label classification. Authors. T. Eftimov, D. Kocev. Publication. AAAI 2019 Spring�...
ABSTRACT. Real-world applications have begun to adopt the multi-label paradigm. The multi-label classification implies an extra.
Missing: Fusion | Show results with:Fusion
... /paper17⫸Vol-2350/paper18. Tome Eftimov Dragi Kocev. Performance Measures Fusion for Experimental Comparison of Methods for Multi-label Classification.
This paper provides a comprehensive empirical investigation of a wide range of MLC methods on a wealth of datasets from different domains.
Sep 2, 2008 � Performance metrics in classification are fundamental in assessing the quality of learning methods and learned models.
Jan 1, 2009 � In this work, we analyse experimentally the behaviour of 18 different performance metrics in several scenarios, identifying clusters and relationships between�...
Missing: Multi- | Show results with:Multi-