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Found 11 papers, 6 papers with code
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can spread to a larger population.
Gradient-based Label Binning in Multi-label Classification
Based on the derivatives computed during training, we dynamically group the labels into a predefined number of bins to impose an upper bound on the dimensionality of the linear system.
Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning
Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent layers of the network.
A Flexible Class of Dependence-aware Multi-Label Loss Functions
For evaluating such predictions, the set of predicted labels needs to be compared to the ground-truth label set associated with that instance, and various loss functions have been proposed for this purpose.
Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions have been proposed.
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far.
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy
We analyze the trade-off between model complexity and accuracy for random forests by breaking the trees up into individual classification rules and selecting a subset of them.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly.
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics
Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained in the training data and it is commonly accepted that the choice of the heuristic has a significant impact on the predictive performance of the learner.
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Exploiting dependencies between labels is considered to be crucial for multi-label classification.
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously.
