Search Results for author:
Found 8 papers, 2 papers with code
CAGE: Causality-Aware Shapley Value for Global Explanations
One way to explain AI models is to elucidate the predictive importance of the input features for the AI model in general, also referred to as global explanations.
Unveiling and unraveling aggregation and dispersion fallacies in group MCDM
We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions.
Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
This paper introduces Bayesian frameworks for tackling various aspects of multi-criteria decision-making (MCDM) problems, leveraging a probabilistic interpretation of MCDM methods and challenges.
Local Explanations for Clinical Search Engine results
In this paper, a model-agnostic explainable method is developed to provide users with further information regarding the reasons why a clinical trial is retrieved in response to a query.
An efficient projection neural network for $\ell_1$-regularized logistic regression
$\ell_1$ regularization has been used for logistic regression to circumvent the overfitting and use the estimated sparse coefficient for feature selection.
SANOM Results for OAEI 2019
Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2019.
Solving the L1 regularized least square problem via a box-constrained smooth minimization
In this paper, an equivalent smooth minimization for the L1 regularized least square problem is proposed.
Comparison of ontology alignment systems across single matching task via the McNemar's test
Ontology alignment is widely-used to find the correspondences between different ontologies in diverse fields. After discovering the alignments, several performance scores are available to evaluate them. The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies. The current trend in the alignment evaluation is to put forward a new score(e. g., precision, weighted precision, etc.
