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Visualizing the Results of Boolean Matrix Factorizations
We provide a method to visualize the results of Boolean Matrix Factorization algorithms. Our method can also be used to visualize overlapping clusters in bipartite graphs. The... -
GLocalX - Explaining in a Local to Global setting
GLocalX is a model-agnostic Local to Global explanation algorithm. Given a set of local explanations expressed in the form of decision rules, and a black-box model to explain,... -
XAI Method for explaining time-series
LASTS is a framework that can explain the decisions of black box models for time series classification. The explanation consists of factual and counterfactual rules revealing... -
LORE
The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of...-
python
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python
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MARLENA
MARLENA is novel technique able to explain the reasons behind any black-box multi-label classifier decision. It will generate an explanation in the form of a decision rule....-
python
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python
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Reducing Graph Structural Bias by Adding shortcut edges
Algorithms that tackle the problem of minimizing average/maximum hitting time (BMAH/BMMH) between different social network groups, given fixed shortcut edges. The...-
Data
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Data
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Visualizing the Results of Biclustering and Boolean Matrix Factorization Algo...
This archive contains the code to visualize biclusters from the paper "Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations" by Thibault Marette, Pauli...
