Visual Explanation of Evidence with Additive Classifiers

No abstract is available for this paper.

• Publication year

  2006

• Venue

  AAAI Conference on Artificial Intelligence

• Publication date

  2006-07-16

• Fields of study

  Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Pattern Classification

  2021cited by this paper
• Pattern Classification

  2012cited by this paper
• Introduction to machine learning

  2004influential reference
• Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations

  2004influential reference
• Nomograms for Visualization of Naive Bayesian Classifier

  2004influential reference
• The Elements of Statistical Learning

  2003influential reference
• A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Exercise Testing)

  2002cited by this paper
• Optimizing search engines using clickthrough data

  2002cited by this paper
• Enhanced word clustering for hierarchical text classification

  2002cited by this paper
• Bayesian Error-Bars for Belief Net Inference

  2001cited by this paper
• Machine learning for medical diagnosis: history, state of the art and perspective

  2001cited by this paper
• The Nature of Statistical Learning Theory

  2000cited by this paper
• Activity monitoring: noticing interesting changes in behavior

  1999cited by this paper
• Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

  1999cited by this paper
• Interpretable Boosted Naïve Bayes Classification

  1998cited by this paper
• Adaptive Fraud Detection

  1997cited by this paper
• An Introduction to the Bootstrap

  1995cited by this paper
• The Nature of Statistical Learning

  1995cited by this paper
• A training algorithm for optimal margin classifiers

  1992cited by this paper
• Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing.

  1992influential reference
• Guidelines for exercise testing. A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Cardiovascular Procedures (Subcommitte on Exercise Testing).

  1986cited by this paper
• XPLAIN: A System for Creating and Explaining Expert Consulting Programs

  1983cited by this paper

• Bridging AI and explainability in civil engineering: the Yin-Yang of predictive power and interpretability

  2025cites this paper
• Real-World Efficacy of Explainable Artificial Intelligence using the SAGE Framework and Scenario-Based Design

  2024cites this paper
• The SAGE Framework for Explaining Context in Explainable Artificial Intelligence

  2024cites this paper
• Evaluating the Effectiveness of Visual Representations of SHAP Values Toward Explainable Artificial Intelligence

  2024influential citation
• INSPIRE: Incorporating Diverse Feature Preferences in Recourse

  2024cites this paper
• From Evidence to Decision: Exploring Evaluative AI

  2024cites this paper
• A survey on XAI and natural language explanations

  2023cites this paper
• Explaining Decisions of Black-Box Models Using BARBE

  2023cites this paper
• Interpretation-based Code Summarization

  2023cites this paper
• Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics

  2023cites this paper
• Towards Usable Neural Comment Generation via Code-Comment Linkage Interpretation: Method and Empirical Study

  2023cites this paper
• What Are the Users’ Needs? Design of a User-Centered Explainable Artificial Intelligence Diagnostic System

  2022cites this paper
• Explanatory machine learning for sequential human teaching

  2022cites this paper
• Privacy-Preserving and Explainable AI in Industrial Applications

  2022cites this paper
• xDBTagger: explainable natural language interface to databases using keyword mappings and schema graph

  2022cites this paper
• From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation

  2022cites this paper
• Interpreting Deep Learning Models for Knowledge Tracing

  2022cites this paper
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

  2021cites this paper
• Why an Android App Is Classified as Malware

  2021cites this paper
• Supporting End-User Debugging of Machine-Learned Programs

  2021cites this paper
• Prediction of Obsessive-Compulsive Disorder: Importance of neurobiology-aided feature design and cross-diagnosis transfer learning.

  2021cites this paper
• Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models

  2021cites this paper
• Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

  2021cites this paper
• Classification of Explainable Artificial Intelligence Methods through Their Output Formats

  2021cites this paper
• Explainable Artificial Intelligence for Tabular Data: A Survey

  2021cites this paper
• Keep CALM and Improve Visual Feature Attribution

  2021cites this paper
• Amortized Generation of Sequential Counterfactual Explanations for Black-box Models

  2021cites this paper
• Mitigating Bias in Algorithmic Systems—A Fish-eye View

  2021cites this paper
• A Note on the Interpretability of Machine Learning Algorithms

  2020cites this paper
• Variable Importance Plots - An Introduction to the vip Package

  2020cites this paper
• Explainable Artificial Intelligence: a Systematic Review

  2020cites this paper
• Towards Interpretable Deep Learning Models for Knowledge Tracing

  2020cites this paper
• Why an Android App is Classified as Malware? Towards Malware Classification Interpretation

  2020cites this paper
• A survey of surveys on the use of visualization for interpreting machine learning models

  2020cites this paper
• A Survey of Interpretability of Machine Learning in Accelerator-based High Energy Physics

  2020cites this paper
• Explainable Robotics in Human-Robot Interactions

  2020cites this paper
• Understanding Patch-Based Learning of Video Data by Explaining Predictions

  2019cites this paper
• Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison

  2019cites this paper
• Survey on Techniques, Applications and Security of Machine Learning Interpretability

  2019influential citation
• Causability and explainability of artificial intelligence in medicine

  2019cites this paper
• Collaborative Explanation of Deep Models with Limited Interaction for Trade Secret and Privacy Preservation

  2019cites this paper
• Deep Neural Model Inspection and Comparison via Functional Neuron Pathways

  2019cites this paper
• Local Decision Pitfalls in Interactive Machine Learning

  2019cites this paper
• Unmasking Clever Hans predictors and assessing what machines really learn

  2019cites this paper
• Visual representation of bug report assignment recommendations

  2019cites this paper
• Patient-Specific Explanations for Predictions of Clinical Outcomes

  2019cites this paper
• Explainable Failure Predictions with RNN Classifiers based on Time Series Data

  2019cites this paper
• Comprehensible Explanation of Predictive Models

  2019cites this paper
• ACE – An Anomaly Contribution Explainer for Cyber-Security Applications

  2019cites this paper
• Unsupervised Detection and Explanation of Latent-class Contextual Anomalies

  2018cites this paper
• A Survey of Methods for Explaining Black Box Models

  2018cites this paper
• Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda

  2018cites this paper
• VisualBackProp: efficient visualization of CNNs

  2018cites this paper
• Explanation of Prediction Models with ExplainPrediction

  2018cites this paper
• Understanding Patch-Based Learning by Explaining Predictions

  2018cites this paper
• RUDDER: Return Decomposition for Delayed Rewards

  2018cites this paper
• Actionable Recourse in Linear Classification

  2018cites this paper
• Measures of Model Interpretability for Model Selection

  2018cites this paper
• Perturbation-Based Explanations of Prediction Models

  2018cites this paper
• VisualBackProp: Efficient Visualization of CNNs for Autonomous Driving

  2018cites this paper
• Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

  2018influential citation
• Explaining Explanations in AI

  2018influential citation
• A Survey of Evaluation Methods and Measures for Interpretable Machine Learning

  2018cites this paper
• Structuring Neural Networks for More Explainable Predictions

  2018cites this paper
• A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

  2018cites this paper
• Interpreting Hierarchichal Data Features - Towards Explainable AI

  2018cites this paper
• Explainable and Interpretable Models in Computer Vision and Machine Learning

  2017cites this paper
• Explaining machine learning models in sales predictions

  2017cites this paper
• Contextialized and personalized explanations of machine learning algorithms

  2017cites this paper
• Methods for interpreting and understanding deep neural networks

  2017cites this paper
• What do we need to build explainable AI systems for the medical domain?

  2017cites this paper
• The State of the Art in Integrating Machine Learning into Visual Analytics

  2017cites this paper
• A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations

  2017cites this paper
• "What is relevant in a text document?": An interpretable machine learning approach

  2016cites this paper
• Evaluating an Assistant for Creating Bug Report Assignment Recommenders

  2016cites this paper
• Deep Taylor Decomposition of Neural Networks

  2016cites this paper
• VisualBackProp: visualizing CNNs for autonomous driving

  2016cites this paper
• Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation

  2016cites this paper
• Explaining nonlinear classification decisions with deep Taylor decomposition

  2015cites this paper
• Superposed Naive Bayes for Accurate and Interpretable Prediction

  2015cites this paper
• Search Strategies for Binary Feature Selection for a Naive Bayes Classifier

  2015cites this paper
• Picturing Bayesian Classifiers: A Visual Data Mining Approach to Parameters Optimization

  2014cites this paper
• Interpretable machine learning and sparse coding for computer vision

  2014cites this paper
• Discrete Bayesian Network Classifiers

  2014cites this paper
• Assisting Software Projects with Bug Report Assignment Recommender Creation

  2014cites this paper
• Detection and Explanation of Statistical Differences Across a Pair of Groups

  2014cites this paper
• Towards crowd-based customer service: a mixed-initiative tool for managing Q&A sites

  2014cites this paper
• Explaining prediction models and individual predictions with feature contributions

  2014cites this paper
• Explanation and Reliability of Individual Predictions

  2013cites this paper
• Efficiently explaining the predictions of a probabilistic radial basis function classification network

  2013cites this paper
• Searching for Credible Relations in Machine Learning

  2013cites this paper
• Explaining inference on a population of independent agents using Bayesian networks

  2013cites this paper
• Intelligible software delivery in smart environments supported by a macro and micro context awareness model

  2013cites this paper
• A framework to support intelligibility in pervasive applications

  2013cites this paper
• Interpreting individual classifications of hierarchical networks

  2013influential citation
• Relative N-gram signatures: Document visualization at the level of character N-grams

  2012cites this paper
• Learning and Explaining the Impact of Enterprises’ Organizational Quality on their Economic Results

  2012cites this paper
• Intelligent tagging systems: machine learning for novel interaction

  2012cites this paper
• The Tag Genome: Encoding Community Knowledge to Support Novel Interaction

  2012cites this paper
• Visually Supported Supervised Machine Learning

  2012cites this paper