141 citations · 597 across the 22 of their papers we have counts for
24 papers
Shapley Chains: Extending Shapley Values to Classifier Chains
Célia Wafa Ayad, Thomas Bonnier, Benjamin Bosch +1
In spite of increased attention on explainable machine learning models, explaining multi-output predictions has not yet been extensively addressed. Methods that use Shapley values…
Transferable Deep Metric Learning for Clustering
Simo Alami. C, Rim Kaddah, Jesse Read
Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric…
Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
Ekaterina Antonenko, Jesse Read
Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is…
Linear TreeShap
Peng Yu, Chao Xu, Albert Bifet +1
Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting…
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
Simo Alami C., Jérémie Decock, Rim Kaddah +1
Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become incre…
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Eran Zvuloni, Jesse Read, Antônio H. Ribeiro +2
Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to fea…