Post-hoc explanation of black-box classifiers using confident itemsets

Techniques for interpretable machine learning

(2019) Mengnan Du et al.   COMMUNICATIONS OF THE ACM

A survey on deep learning for big data

(2018) Qingchen Zhang et al.   Information Fusion

A Survey of Methods for Explaining Black Box Models

(2018) Riccardo Guidotti et al.   ACM COMPUTING SURVEYS

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

Explaining nonlinear classification decisions with deep Taylor decomposition

(2017) Grégoire Montavon et al.   PATTERN RECOGNITION

An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums

(2017) Jun Gao et al.   Journal of Healthcare Engineering

Explaining Data-Driven Document Classifications

(2017) David Martens et al.   MIS QUARTERLY

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

(2015) Benjamin Letham et al.   Annals of Applied Statistics

Proposing a classifier ensemble framework based on classifier selection and decision tree

(2015) Hamid Parvin et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One

A new classifier ensemble methodology based on subspace learning

(2012) Hamid Parvin et al.   JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE