Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Dimensionality Reduction based on SHAP Analysis: A Simple and Trustworthy Approach
36
Zitationen
5
Autoren
2020
Jahr
Abstract
In this 21st century the world is driven by data, analysis, and predictions based on this data is substantial. However, these predictions that have an immense impact on our daily life comes with an overhead of complex data mining and large datasets. With this paper, we will suggest a way to reduce the dimensionality of the dataset without a great loss of accuracy and reduce the necessity for complex data mining, by analyzing the features based on their SHAP - SHapley Additive explanation, values we prioritize the features and discard the features of unsubstantial relevance to the accuracy of the model.
Ähnliche Arbeiten
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2017 · 21.065 Zit.
Generative Adversarial Nets
2023 · 19.896 Zit.
Visualizing and Understanding Convolutional Networks
2014 · 15.382 Zit.
"Why Should I Trust You?"
2016 · 14.801 Zit.
Generative adversarial networks
2020 · 13.384 Zit.