Threshold ML-KNN: Statistical evaluation on multiple benchmarks

0Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This paper concerns the performance of a recently proposed multi-label classification algorithm called Threshold ML-KNN. It is a modification of the established ML-KNN algorithm. The performance of both algorithms is compared on several publicly available benchmarks. Based on the results, the conclusion is drawn that Threshold ML-KNN is statistically significantly better in terms of accuracy, f-measure and hamming loss. © 2013 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Łukasik, M., & Sydow, M. (2013). Threshold ML-KNN: Statistical evaluation on multiple benchmarks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7912 LNCS, pp. 198–205). https://doi.org/10.1007/978-3-642-38634-3_22

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free