TY  - JOUR
ID  - loza16MLRL
T1  - Learning rules for multi-label classification: a stacking and a separate-and-conquer approach
A1  - Loza Mencía, Eneldo
A1  - Janssen, Frederik
ED  - D{\v z}eroski, Sašo
ED  - Kocev, Dragi
ED  - Panov, Panče
JA  - Machine Learning
Y1  - 2016
VL  - 105
IS  - 1
SP  - 77
EP  - 126
SN  - 0885-6125
UR  - https://www.ke.tu-darmstadt.de/publications/papers/loza16MLRL.pdf
M2  - doi: 10.1007/s10994-016-5552-1
KW  - Label Dependencies
KW  - multilabel classification
KW  - Rule Learning
KW  - Stacking
N2  - Dependencies between the labels are commonly regarded as the crucial issue in multi-label classification. Rules provide a natural way for symbolically describing such relationships. For instance, rules with label tests in the body allow for representing directed dependencies like implications, subsumptions, or exclusions. Moreover, rules naturally allow to jointly capture both local and global label dependencies. In this paper, we introduce two approaches for learning such label-dependent rules. Our first solution is a bootstrapped stacking approach which can be built on top of a conventional rule learning algorithm. For this, we learn for each label a separate ruleset, but we include the remaining labels as additional attributes in the training instances. The second approach goes one step further by adapting the commonly used separate-and-conquer algorithm for learning multi-label rules. The main idea is to re-include the covered examples with the predicted labels so that this information can be used for learning subsequent rules. Both approaches allow for making label dependencies explicit in the rules. In addition, the usage of standard rule learning techniques targeted at producing accurate predictions ensures that the found rules are useful for actual classification. Our experiments show (a) that the discovered dependencies contribute to the understanding and improve the analysis of multi-label datasets, and (b) that the found multi-label rules are crucial for the predictive performance as our proposed approaches beat the baseline using conventional rules.
ER  -