Scalable rule-based representation learning for interpretable classification
Rule-based models, eg, decision trees, are widely used in scenarios demanding high model
interpretability for their transparent inner structures and good model expressivity. However …
interpretability for their transparent inner structures and good model expressivity. However …
Wrongdoing monitor: A graph-based behavioral anomaly detection in cyber security
The so-called behavioral anomaly detection (BAD) is expected to solve effectively a variety
of security issues by detecting the deviances from normal behavioral patterns of protected …
of security issues by detecting the deviances from normal behavioral patterns of protected …
Excelformer: A neural network surpassing gbdts on tabular data
Data organized in tabular format is ubiquitous in real-world applications, and users often
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
Neuro-symbolic interpretable collaborative filtering for attribute-based recommendation
Recommender System (RS) is ubiquitous on today's Internet to provide multifaceted
personalized information services. While an enormous success has been made in pushing …
personalized information services. While an enormous success has been made in pushing …
Learning interpretable rules for scalable data representation and classification
Rule-based models, eg, decision trees, are widely used in scenarios demanding high model
interpretability for their transparent inner structures and good model expressivity. However …
interpretability for their transparent inner structures and good model expressivity. However …
Feature-Enhanced Neural Collaborative Reasoning for Explainable Recommendation
Providing reasonable explanations for a specific suggestion given by the recommender can
help users trust the system more. As logic rule-based inference is concise, transparent, and …
help users trust the system more. As logic rule-based inference is concise, transparent, and …
Can a Deep Learning Model be a Sure Bet for Tabular Prediction?
Data organized in tabular format is ubiquitous in real-world applications, and users often
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
A Comprehensive Survey on Self-Interpretable Neural Networks
Neural networks have achieved remarkable success across various fields. However, the
lack of interpretability limits their practical use, particularly in critical decision-making …
lack of interpretability limits their practical use, particularly in critical decision-making …
Explainable neural rule learning
Although neural networks have achieved great successes in various machine learning
tasks, people can hardly know what neural networks learn from data due to their black-box …
tasks, people can hardly know what neural networks learn from data due to their black-box …
Fuzzy Neural Logic Reasoning for Robust Classification
G Lin, Y Zhang - ACM Transactions on Knowledge Discovery from Data, 2024 - dl.acm.org
The efficacy of neural networks is widely recognized across a multitude of machine learning
tasks, yet their black-box nature impedes the understanding of their decision-making …
tasks, yet their black-box nature impedes the understanding of their decision-making …