Search Results for author:
Found 8 papers, 2 papers with code
Aequitas Flow: Streamlining Fair ML Experimentation
Aequitas Flow is an open-source framework for end-to-end Fair Machine Learning (ML) experimentation in Python.
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
The system can present global explanations for the black box model and local explanations for why the black box model predicts a failure.
Super-Resolution Analysis for Landfill Waste Classification
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts.
Explainable Predictive Maintenance
We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
A Benchmark dataset for predictive maintenance
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal.
Model Optimization in Imbalanced Regression
In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks.
UBL: an R package for Utility-based Learning
The main goal of UBL package is to facilitate the utility-based predictive analytic task by providing a set of methods to deal with this type of problems in the R environment.
