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Found 17 papers, 9 papers with code
Combining Predictions under Uncertainty: The Case of Random Decision Trees
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.
Tree-Based Dynamic Classifier Chains
However, the method requires a fixed, static order of the labels.
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.
Revisiting Non-Specific Syndromic Surveillance
Infectious disease surveillance is of great importance for the prevention of major outbreaks.
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.
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.
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Our results on synthetic data show that it is challenging to improve the performance with a trainable fusion method based on machine learning.
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.
Analysis and Optimization of Deep Counterfactual Value Networks
Recently a strong poker-playing algorithm called DeepStack was published, which is able to find an approximate Nash equilibrium during gameplay by using heuristic values of future states predicted by deep neural networks.
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Multi-label classification is the task of predicting a set of labels for a given input instance.
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer.
