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Found 6 papers, 3 papers with code
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
In addressing this challenge, especially in complex scenarios, we introduce the Decision Predicate Graph (DPG) as a model-agnostic tool to provide a global interpretation of the model.
Tailoring Machine Learning for Process Mining
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction.
CoSMo: a Framework to Instantiate Conditioned Process Simulation Models
This architecture facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase as an attempt to fill the gap of incorporating information into deep learning models to perform what-if analysis.
Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters
Our approach is grounded on a small set of optimized values able to obtain predictive performance values better than default settings provided by popular tools.
Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms
DT induction algorithms present high predictive performance and interpretable classification models, though many HPs need to be adjusted.
Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining
Thus, SVFDT is a suitable option for data stream mining with memory and time limitations, recommended as a weak learner in ensemble-based solutions.
