Interpretable Classifier

Interpretable classifiers aim to improve the transparency and understandability of machine learning models while maintaining high predictive accuracy. Current research focuses on developing inherently interpretable models, such as rule-based systems, decision trees, and specific neural network architectures designed for explainability (e.g., those incorporating attention mechanisms or concept prototypes), as well as enhancing the interpretability of existing black-box models through post-hoc explanation techniques. This field is crucial for building trust in AI systems, particularly in high-stakes applications like healthcare and finance, where understanding model decisions is paramount for responsible deployment and effective human-AI collaboration.

Papers

October 13, 2024

Learning Interpretable Classifiers for PDDL Planning
Arnaud Lequen
Task Planning Temporal Logic Classical Planning Interpretable Classifier Behavior Classification GOFAI Based Planning

October 1, 2024

Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition
Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich, Simon Bacon, Eric Granger
Facial Expression Recognition Expression Recognition Class Activation Interpretable Classifier Interpretable Layer Interpretable Deep Action Cue

September 25, 2024

Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware
Ishan Karunanayake, Mashael AlSabah, Nadeem Ahmed, Sanjay Jha
Malware Attack Malware Classification Interpretable Classifier Malicious Traffic Rat Image ToR Network

August 21, 2024

Neural Symbolic Logical Rule Learner for Interpretable Learning
Bowen Wei, Ziwei Zhu
Negation Detection Interpretable Classifier Rule Learning Learning Rule Symbolic Rule Interpretable Learning Discrete Activation

August 15, 2024

Classification of High-dimensional Time Series in Spectral Domain using Explainable Features
Sarbojit Roy, Malik Shahid Sultan, Hernando Ombao
Classification Code Inherent Interpretability Interpretable Classifier Discriminative Task High Dimensional Time Series EEG Datasets Spectral Domain Explainable Feature

August 13, 2024

IFShip: A Large Vision-Language Model for Interpretable Fine-grained Ship Classification via Domain Knowledge-Enhanced Instruction Tuning
Mingning Guo, Mengwei Wu, Yuxiang Shen, Haifeng Li, Chao Tao
Large Vision Language Model Instruction Tuning Internet Service Domain Interpretable Classifier Reaction Ship Ship Dataset

July 12, 2024

Integrating White and Black Box Techniques for Interpretable Machine Learning
Eric M. Vernon, Naoki Masuyama, Yusuke Nojima
Inherent Interpretability Black Box Interpretable Machine Learning Ensemble Classifier Interpretable Classifier Random Forest Classifier

July 9, 2024

Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs
Christian Riefolo, Nicola Fanizzi, Claudia d'Amato
Knowledge Graph Bayesian Perspective Description Logic Interpretable Classifier Probabilistic Classifier Naive Bayes Classifier

June 20, 2024

Behavior-Inspired Neural Networks for Relational Inference
Yulong Yang, Bowen Feng, Keqin Wang, Naomi Leonard, Adji Bousso Dieng, Christine Allen-Blanchette
Graph Drawing Opinion Dynamic Relational Reasoning Complex Dynamic Interpretable Classifier

June 11, 2024

Logical Distillation of Graph Neural Networks
Alexander Pluska, Pascal Welke, Thomas Gärtner, Sagar Malhotra
Graph Neural Network Interpretable Model Interpretable Classifier First Order Logic Reasoning Distillation

June 3, 2024

Globally Interpretable Classifiers via Boolean Formulas with Dynamic Propositions
Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
Inherent Interpretability High Explainability Answer Set Programming Interpretable Classifier Global Interpretability Boolean Algebraic Manipulation

May 28, 2024

Interpretable classification of wiki-review streams
Silvia García Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo Rial
Natural Language Processing Content Based Feature Interpretable Classifier Travel Domain Crowdsourcing Model

April 13, 2024

MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes
Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen Chiu
Interpretable Model ATAC Net Interpretable Classifier Level Explanation Multi Prototype

February 10, 2024

Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
Tajima Shinji, Ren Sugihara, Ryota Kitahara, Masayuki Karasuyama
Interpretable Model Tree Search Interpretable Classifier Graph Level Prediction

February 8, 2024

Interpretable classifiers for tabular data via discretization and feature selection
Reijo Jaakkola, Tomi Janhunen, Antti Kuusisto, Masood Feyzbakhsh Rankooh, Miikka Vilander
Inherent Interpretability Feature Selection Tabular Data Simple Classifier Data Discretization Interpretable Classifier Boolean Classifier

February 4, 2024

Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition
Kseniya Akhalaya, Franziska Nestler, Daniel Potts
Support Vector Machine Interpretable Classifier Fast Fourier Transform Norm Regularization

February 1, 2024

Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich, Simon Bacon, Eric Granger
Facial Expression Recognition Expression Recognition Interpretable Deep Learning Interpretable Classifier Interpretable Deep Interpretable Layer Action Cue

November 24, 2023

Towards Interpretable Classification of Leukocytes based on Deep Learning
Stefan Röhrl, Johannes Groll, Manuel Lengl, Simon Schumann, Christian Klenk, Dominik Heim, Martin Knopp, Oliver Hayden, Klaus Diepold
Deep Learning Machine Learning Confidence Estimation Label Free Interpretable Classifier Cytology Image White Blood Cell

October 30, 2023

Faithful and Robust Local Interpretability for Textual Predictions
Gianluigi Lopardo, Frederic Precioso, Damien Garreau
Inherent Interpretability Robust Version Counterfactual Explanation Interpretable Classifier Robust Explanation Textual Model Local Interpretability

October 1, 2023

Counterfactual Image Generation for adversarially robust and interpretable Classifiers
Rafael Bischof, Florian Scheidegger, Michael A. Kraus, A. Cristiano I. Malossi
Adversarial Attack Generative Adversarial Network Adversarial Sample Counterfactual Image Interpretable Classifier