Exploring Evaluation Methods for Interpretable Machine Learning: A Survey

Exploring Evaluation Methods for Interpretable Machine Learning: A Survey

In recent times, the progress of machine learning has facilitated the development of decision support systems that exhibit predictive accuracy, surpassing human capabilities in certain scenarios. However, this improvement has come at the cost of increased model complexity, rendering them black-box m...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς:	Nourah Alangari, Mohamed El Bachir Menai, Hassan Mathkour, Ibrahim Almosallam
Μορφή:	Άρθρο
Γλώσσα:	English
Έκδοση:	MDPI AG 2023-08-01
Σειρά:	Information
Θέματα:	interpretability explainable AI evaluating interpretability
Διαθέσιμο Online:	https://www.mdpi.com/2078-2489/14/8/469

Παρόμοια τεκμήρια

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Opening the Black-Box: Extracting Medical Reasoning from Machine Learning Predictions
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Έκδοση: (2021-09-01)

Effects of Class Imbalance Countermeasures on Interpretability
ανά: David Cemernek, κ.ά.
Έκδοση: (2024-01-01)

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
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Έκδοση: (2021-12-01)

Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma
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Έκδοση: (2022-09-01)

Explainability of deep learning models in medical video analysis: a survey
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Έκδοση: (2023-03-01)

TS-CNN: A Three-Tier Self-Interpretable CNN for Multi-Region Medical Image Classification
ανά: V. A. Ashwath, κ.ά.
Έκδοση: (2023-01-01)

A Framework for Interpretability in Machine Learning for Medical Imaging
ανά: Alan Q. Wang, κ.ά.
Έκδοση: (2024-01-01)

A Scoping Review on the Progress, Applicability, and Future of Explainable Artificial Intelligence in Medicine
ανά: Raquel González-Alday, κ.ά.
Έκδοση: (2023-09-01)

An Empirical Comparison of Interpretable Models to Post-Hoc Explanations
ανά: Parisa Mahya, κ.ά.
Έκδοση: (2023-05-01)

Exploring the Impact of Backbone Architecture on Explainable CNNs’ Interpretability
ανά: Ábel Portik, κ.ά.
Έκδοση: (2024-10-01)

Explainable AI: A Neurally-Inspired Decision Stack Framework
ανά: Muhammad Salar Khan, κ.ά.
Έκδοση: (2022-09-01)

XAI GNSS—A Comprehensive Study on Signal Quality Assessment of GNSS Disruptions Using Explainable AI Technique
ανά: Arul Elango, κ.ά.
Έκδοση: (2024-12-01)

Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
ανά: Junkang An, κ.ά.
Έκδοση: (2023-07-01)

Interpretable molecular encodings and representations for machine learning tasks
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Έκδοση: (2024-12-01)

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications
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Έκδοση: (2024-10-01)

Explainable AI: A Review of Machine Learning Interpretability Methods
ανά: Pantelis Linardatos, κ.ά.
Έκδοση: (2020-12-01)

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method
ανά: Fa Li, κ.ά.
Έκδοση: (2024-10-01)

Interpretable machine learning model to predict survival days of malignant brain tumor patients
ανά: Snehal Rajput, κ.ά.
Έκδοση: (2023-01-01)

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
ανά: Amirata Ghorbani, κ.ά.
Έκδοση: (2021-12-01)

A detailed study of interpretability of deep neural network based top taggers
ανά: Ayush Khot, κ.ά.
Έκδοση: (2023-01-01)

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders
ανά: Ho Heon Kim, κ.ά.
Έκδοση: (2024-03-01)

Interpretable deep learning for consistent large-scale urban population estimation using Earth observation data
ανά: Sugandha Doda, κ.ά.
Έκδοση: (2024-04-01)

New unified insights on deep learning in radiological and pathological images: Beyond quantitative performances to qualitative interpretation
ανά: Yoichi Hayashi
Έκδοση: (2020-01-01)

The Role of Human Knowledge in Explainable AI
ανά: Andrea Tocchetti, κ.ά.
Έκδοση: (2022-07-01)

Understanding the Behavior of Gas Sensors Using Explainable AI
ανά: Sanghamitra Chakraborty, κ.ά.
Έκδοση: (2022-11-01)

Explaining the solutions of the unit commitment with interpretable machine learning
ανά: Sara Lumbreras, κ.ά.
Έκδοση: (2024-09-01)

Interpreting the decisions of CNNs via influence functions
ανά: Aisha Aamir, κ.ά.
Έκδοση: (2023-07-01)

Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques – A Review
ανά: Lukas Baur, κ.ά.
Έκδοση: (2024-05-01)

Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN’s Learned Features
ανά: Kyle A. Hasenstab, κ.ά.
Έκδοση: (2023-01-01)

Analyzing and Explaining Black-Box Models for Online Malware Detection
ανά: Harikha Manthena, κ.ά.
Έκδοση: (2023-01-01)

AudioProtoPNet: An interpretable deep learning model for bird sound classification
ανά: René Heinrich, κ.ά.
Έκδοση: (2025-07-01)

Editorial: Explainable Artificial Intelligence (XAI) in Systems Neuroscience
ανά: Angela Lombardi, κ.ά.
Έκδοση: (2021-10-01)

Correcting gradient-based interpretations of deep neural networks for genomics
ανά: Antonio Majdandzic, κ.ά.
Έκδοση: (2023-05-01)

A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence
ανά: Mi-Young Kim, κ.ά.
Έκδοση: (2021-11-01)

ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations
ανά: Christina Humer, κ.ά.
Έκδοση: (2022-04-01)

Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction
ανά: Fabian Obster, κ.ά.
Έκδοση: (2024-01-01)

Towards interpreting machine learning models for predicting soil moisture droughts
ανά: Feini Huang, κ.ά.
Έκδοση: (2023-01-01)

A Systematic Literature Review on AI Safety: Identifying Trends, Challenges, and Future Directions
ανά: Wissam Salhab, κ.ά.
Έκδοση: (2024-01-01)