5 papers
A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation
Keita Kinjo
Counterfactual explanations (CEs) enhance the interpretability of machine learning models by identifying the smallest change to an input required to obtain a desired output. Althou…
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
Keita Kinjo
In recent years, explainability in machine learning has gained importance. In this context, counterfactual explanation (CE), which is an explanation method that uses examples, has…
Counterfactual Explanation for Multivariate Time Series Forecasting with Exogenous Variables
Keita Kinjo
Currently, machine learning is widely used across various domains, including time series data analysis. However, some machine learning models function as black boxes, making interp…
Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Data
Keita Kinjo
Recently, the proliferation of omni-channel platforms has attracted interest in customer journeys, particularly regarding their role in developing marketing strategies. However, fe…
Diversity and Inclusion Index with Networks and Similarity: Analysis and its Application
Keita Kinjo
In recent years, the concepts of ``diversity'' and ``inclusion'' have attracted considerable attention across a range of fields, encompassing both social and biological disciplines…