.png){kind=icon}
Return
Multi-label learning with label-specific feature reduction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2016.04.012.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In multi-label learning, since different labels may have some distinct characteristics of their own, multi label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Feature reduction
Fuzzy rough set
Label-specific feature
Multi-label learning
Sample selection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
High-pressure synthesis of [Ru2(μ-dppm)2 (μ-CO)(CO4, Ru2(μ-dppm)(μ-dmpm) (μ-CO)(CO4], and [Ru(dcypm)(CO)3] from Ru3(CO)12 and the diphosphines: R2PCH2PR2; R = CH3(dmpm), C6H5(dppm) and C6H11 (dcypm). Crystal and molecular structures of [Ru2(μ-dppm)2(μ-CO)(CO)4], and [Ru(dcypm)(CO)3].png)
Polyhedron
PolyhedronIF0
PREAI
ShareSave
ShareSave
An eye disease in silver carp, Hypophthalmichthys molitrix, held in tropical ponds, associated with the bacterium Staphylococcus aureusAquaculture
AquacultureIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more