An overview of advances in reliability estimation of individual predictions in machine learning

No abstract is available for this paper.

• Publication year

  2009

• Venue

  Intelligent Data Analysis

• Publication date

  2009-03-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3233/IDA-2009-0371
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Automatic selection of reliability estimates for individual regression predictions

  2010cited by this paper
• Learning from Labeled and Unlabeled Data

  2010cited by this paper
• Data Analysis Using Regression and Multilevel/Hierarchical Models

  2009cited by this paper
• Comparison of approaches for estimating reliability of individual regression predictions

  2008cited by this paper
• Towards Reliable Reliability Estimates for Individual Regression Predictions Technical Report T-01 / 08

  2008cited by this paper
• Estimation of Regressor Reliability

  2008cited by this paper
• Introduction to Sensitivity Analysis

  2008cited by this paper
• Estimation of individual prediction reliability using the local sensitivity analysis

  2008cited by this paper
• Machine Learning and Data Mining: Introduction to Principles and Algorithms

  2007cited by this paper
• Stream-Based Electricity Load Forecast

  2007cited by this paper
• Cluster Quality Indexes for Symbolic Classification - An Examination

  2006cited by this paper
• Open set face recognition using transduction

  2005cited by this paper
• Sensitivity analysis for chemical models.

  2005cited by this paper
• Assessing Cluster Quality Using Multiple Measures - A Decision Tree Based Approach

  2005cited by this paper
• The Role of Unlabeled Data in Supervised Learning

  2004cited by this paper
• Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

  2004cited by this paper
• Evaluation of prediction reliability in regression using the transduction principle

  2003cited by this paper
• Leave-one-out error and stability of learning algorithms with applications

  2002cited by this paper
• Stochastic learning methods for dynamic neural networks: simulated and real-data comparisons

  2002cited by this paper
• Stability and Generalization

  2002cited by this paper
• Reliable Classifications with Machine Learning

  2002cited by this paper
• Data perturbation for escaping local maxima in learning

  2002cited by this paper
• Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

  2001cited by this paper
• Experimental designs for sensitivity analysis of simulation models

  2001cited by this paper
• Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments

  2001cited by this paper
• Dynamic classifier selection based on multiple classifier behaviour

  2001cited by this paper
• Ridge Regression Confidence Machine

  2001cited by this paper
• Dual perturb and combine algorithm

  2001cited by this paper
• Enhancing Supervised Learning with Unlabeled Data

  2000cited by this paper
• Algorithmic Stability and Generalization Performance

  2000cited by this paper
• The Nature of Statistical Learning Theory

  2000cited by this paper
• Making Sensitivity Analysis Computationally Efficient

  2000cited by this paper
• The Covariance Inflation Criterion for Adaptive Model Selection

  1999cited by this paper
• Boosting methodology for regression problems

  1999cited by this paper
• The State of Boosting ∗

  1999cited by this paper
• Confidence and prediction intervals for neural network ensembles

  1999cited by this paper
• Transduction with Confidence and Credibility

  1999influential reference
• Model Search by Bootstrap “Bumping”

  1999cited by this paper
• A Brief Introduction to Boosting

  1999cited by this paper
• Constructive Incremental Learning from Only Local Information

  1998cited by this paper
• Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data

  1998cited by this paper
• Learning by Transduction

  1998cited by this paper
• Local learning for data analysis

  1998cited by this paper
• Improving Regressors using Boosting Techniques

  1997cited by this paper
• A decision-theoretic generalization of on-line learning and an application to boosting

  1997cited by this paper
• Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation

  1997cited by this paper
• Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series))

  1997cited by this paper
• Active Learning with Statistical Models

  1996cited by this paper
• Practical Confidence and Prediction Intervals

  1996influential reference
• Bagging Predictors

  1996cited by this paper
• Combination of multiple classifiers using local accuracy estimates

  1996cited by this paper
• Pasting Bites Together For Prediction In Large Data Sets And On-Line

  1996cited by this paper
• REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS

  1995cited by this paper
• An Introduction to the Bootstrap

  1995cited by this paper
• Model Search and Inference By Bootstrap "bumping

  1995cited by this paper
• Predictions with Confidence Intervals ( Local Error Bars )

  1994cited by this paper
• Learning Classification with Unlabeled Data

  1993cited by this paper
• Assessing the Quality of Learned Local Models

  1993cited by this paper
• Supervised learning from incomplete data via an EM approach

  1993cited by this paper
• Stacked generalization

  1992cited by this paper
• Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions

  1992cited by this paper
• A Complexity Analysis of Cooperative Mechanisms in Reinforcement Learning

  1991cited by this paper
• Active Exploration in Dynamic Environments

  1991cited by this paper
• Training Connectionist Networks with Queries and Selective Sampling

  1989cited by this paper
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

  1977cited by this paper

• Evaluating example-based explainable artificial intelligence methods for breast cancer diagnosis

  2025cites this paper
• Dynamic variance computation for improved model reliability: A non-parametric approach for out-of-sample predictions

  2025cites this paper
• A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application

  2025cites this paper
• Towards a Comprehensive Taxonomy of Pointwise Reliability Assessment Techniques for Machine Learning Models

  2025influential citation
• Reliability estimation for individual predictions in machine learning systems: A model reliability-based approach

  2024cites this paper
• Knowledge-based reliability assessment of models with application to risk stratification

  2024cites this paper
• Quantitative assessment of machine learning reliability and resilience

  2024cites this paper
• Redefining Trust: Assessing Reliability of Machine Learning Algorithms in Intrusion Detection Systems

  2024cites this paper
• Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

  2024cites this paper
• Reliability assessment of models for risk stratification: A knowledge-based approach

  2024cites this paper
• Pointwise Reliability Assessment: Density vs. Local Fit Methods

  2024cites this paper
• Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing

  2024cites this paper
• Automated Gaze-Based Identification of Students’ Strategies in Histogram Tasks through an Interpretable Mathematical Model and a Machine Learning Algorithm

  2023cites this paper
• Uncertainty-Based Rejection in Machine Learning: Implications for Model Development and Interpretability

  2022cites this paper
• A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods

  2022cites this paper
• StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance Measures

  2022cites this paper
• Towards risk-aware artificial intelligence and machine learning systems: An overview

  2022cites this paper
• Evaluating pointwise reliability of machine learning prediction

  2022cites this paper
• Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

  2021cites this paper
• Renewable Energy Systems Prognostics and Health Management: A Review of Recent Advances

  2021cites this paper
• Statistical perspectives on reliability of artificial intelligence systems

  2021influential citation
• An Optimized Machine Learning Approach for Predicting Parkinson's Disease

  2021cites this paper
• Stability Metrics for Enhancing the Evaluation of Outcome-Based Business Process Predictive Monitoring

  2021cites this paper
• Conformal Prediction with Orange

  2021cites this paper
• A Methodology for Non-Functional Property Evaluation of Machine Learning Models

  2020cites this paper
• Rebooting data-driven soft-sensors in process industries: A review of kernel methods

  2020cites this paper
• Identifying unreliable predictions in clinical risk models

  2020cites this paper
• A hybrid reliability metric for SLA predictive monitoring

  2019cites this paper
• Non-Functional Requirements for Machine Learning: Challenges and New Directions

  2019cites this paper
• Does Your Accurate Process Predictive Monitoring Model Give Reliable Predictions?

  2018cites this paper
• Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

  2018cites this paper
• State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

  2017cites this paper
• Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

  2016cites this paper
• A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

  2016cites this paper
• Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?

  2015cites this paper
• Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

  2015cites this paper
• Probabilistic Reframing for Cost-Sensitive Regression

  2014cites this paper
• Probabilistic Reframing for Cost-Sensitive Regression

  2014cites this paper
• Input dependent prediction intervals for supervised regression

  2014cites this paper
• Applicability Domain Dependent Predictive Uncertainty in QSAR Regressions

  2014cites this paper
• Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge

  2014cites this paper
• Proteochemometric modeling in a Bayesian framework

  2014cites this paper
• A robust and reliable data-driven prognostics approach based on Extreme Learning Machine and Fuzzy Clustering

  2014cites this paper
• Quantifying the reliability of fault classifiers

  2014cites this paper
• Enhancing data stream predictions with reliability estimators and explanation

  2014cites this paper
• The Use of Prediction Reliability Estimates on Imbalanced Datasets: A Case Study of Wall Shear Stress in the Human Carotid Artery Bifurcation

  2013cites this paper
• Applying the generalized autoregressive conditional Heteroskedastic model to analyze and forecast the field failure data of repairable systems

  2013cites this paper
• Uncertainty in QSAR Predictions

  2013cites this paper
• Soft (Gaussian CDE) regression models and loss functions

  2012cites this paper
• Soft (Gaussian CDE) regression models and loss functions

  2012cites this paper
• No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

  2012cites this paper
• Estimating Reliability for Assessing and Correcting Individual Streaming Predictions

  2012cites this paper
• Sample-Dependent Feature Selection for Faster Document Image Categorization

  2011cites this paper
• Reliability Estimates for Regression Predictions: Performance Analysis

  2011cites this paper
• Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation Models

  2010cites this paper
• Single-Stacking Conformity Approach to Reliable Classification

  2010cites this paper
• Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs

  2010cites this paper
• Explanation and reliability of prediction models: the case of breast cancer recurrence

  2010cites this paper
• k-Version-Space Multi-class Classification Based on k-Consistency Tests

  2010cites this paper
• Online Reliability Estimates for Individual Predictions in Data Streams

  2008cites this paper