Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns

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 multilabel 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.