Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Multilabel classification via calibrated label ranking
Machine Learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Guest Editorial: Global modeling using local patterns
Data Mining and Knowledge Discovery
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On the stratification of multi-label data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Non-redundant subgroup discovery in large and complex data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A Metric for Comparing Relational Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
The straightforward approach to multi-label classification is based on decomposition, which essentially treats all labels independently and ignores interactions between labels. We propose to enhance multi-label classifiers with features constructed from local patterns representing explicitly such interdependencies. An Exceptional Model Mining instance is employed to find local patterns representing parts of the data where the conditional dependence relations between the labels are exceptional. We construct binary features from these patterns that can be interpreted as partial solutions to local complexities in the data. These features are then used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of decompositive multi-label learning techniques.