Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning…
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48 results for “Prediction Results”
New tree splitting criteria improve probabilistic predictions.
problem Improving tree-based nonparametric predictive distributions.
method Using proper scoring rules for tree splitting criteria.
result Trees with new splitting criteria produce better predictive distributions.
The paper presents a method for generating well-calibrated prediction intervals using quality-driven deep ensembles.
problem Generating reliable prediction intervals for regression analysis.
method A multi-objective loss function combining quality measures for prediction intervals and point estimates, with a penalty function to ensure semantic integrity and stability.
result The method produces well-calibrated prediction intervals and point estimates, capturing both aleatoric and epistemic uncertainty.
COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.
problem Overly conservative prediction sets in online conformal prediction methods when data distribution shifts.
method Conformal Optimistic Prediction (COP) incorporating estimated cumulative distribution function of non-conformity scores.
result COP produces tighter prediction sets with valid coverage guarantees, outperforming other methods.
Neural networks for stock price prediction often misrepresent model performance due to flawed error metrics.
problem Flawed prediction error metrics lead to unreliable model evaluations in the securities market.
method Used data from 20 stock datasets across multiple markets and evaluated with four prediction error measures.
result Prediction error value only partially reflects model accuracy and fails to represent stock price direction.
PBI inference may not be calibrated if predictive model is inaccurate.
problem Uncertainty quantification in PBI may be unreliable if the predictive model is not accurate.
method Predictive Bayesian inference with a forward predictive model.
result Posterior concentration depends on the predictive model, leading to potential calibration issues.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
problem Evaluating the quality of joint predictions generated by neural agents.
method Developed an open-source benchmark (The Neural Testbed) to assess agents' marginal and joint predictions.
result Popular Bayesian deep learning agents perform poorly on joint predictions, even with accurate marginal predictions.
In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of aftereffect or memory. The technology proposes prediction with the hierarchy of time discretization intervals a…
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and …
Proposes a method to create prediction intervals for neural networks using cross-validation.
problem Lack of prediction intervals for neural networks.
method k-fold cross-validation to construct conformal prediction intervals.
result Proposed method produces narrower intervals with similar coverage compared to SC method.
Study on how imprecise medical data affects predictions in hyperthyroidism.
problem Impact of imprecise medical data on prediction results.
method Formulated a model for data imprecisions, generated imprecise samples, defined measures to evaluate impacts, and performed experiments.
result Small imprecisions can lead to large ranges of predicted results, potentially causing mis-labeling and inappropriate actions.
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with confl…
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
problem The need for accurate joint predictions in decision-making problems.
method The paper analyzes combinatorial decision problems, sequential predictions, and multi-armed bandits, introducing an approximate Thompson sampling algorithm and new regret bounds.
result Accurate joint predictions are essential for good performance in decision-making problems.
This paper improves fund net value prediction using ARIMA-LSTM hybrid model.
problem Inaccurate prediction of fund net value due to linear characteristics of models.
method ARIMA-LSTM hybrid model: ARIMA for linear characteristics, LSTM for nonlinear.
result Hybrid model shows higher accuracy in predicting fund net value.
FAB-PPI uses prior knowledge to improve prediction-powered inference.
problem Improving statistical inference with machine learning predictions.
method Informing PPI with prior knowledge on prediction quality.
result FAB-PPI improves inference accuracy and confidence intervals.
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
problem Improving out-of-distribution coverage in multiclass image classification.
method Combining Bayesian deep learning with split conformal prediction methods.
result Combining methods can reduce out-of-distribution coverage in some cases.
We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based…
This paper discusses a counterpart of conformal prediction for e-values, conformal e-prediction. Conformal e-prediction is conceptually simpler and had been developed in the 1990s as a precursor of conformal prediction. When conformal prediction emerged as result of replacing e-values by p-values, it seemed to have imp…
Prediction models can harm patients even when accurate, leading to self-fulfilling prophecies.
problem Prediction models can lead to harmful decisions that worsen patient outcomes.
method Formal characterization of harmful prediction models and analysis of their impact.
result Well-calibrated models are ineffective for decision-making as they do not change the data distribution.
In this paper a new method for heat load prediction in district energy systems is proposed. The method uses a nominal model for the prediction of the outdoor temperature dependent space heating load, and a data driven latent variable model to predict the time dependent residual heat load. The residual heat load arises …
The study compares DLS method with machine learning for cricket match result prediction.
problem Improving accuracy of Duckworth-Lewis-Stern method for cricket match result prediction.
method Comparison of Duckworth-Lewis-Stern method with various supervised learning algorithms and optimization of DLS resource table.
result Development of Unpredictability Index to rank nations based on unpredictability in ODI matches.
This work reviews and evaluates methods for predicting prediction intervals in regression problems.
problem Calibration of prediction intervals in regression problems.
method Four classes of methods: Bayesian, ensemble, direct interval estimation, and conformal prediction.
result Conformal prediction can be used as a general calibration procedure.
We discuss the finite sample theoretical properties of online predictions in non-stationary time series under model misspecification. To analyze the theoretical predictive properties of statistical methods under this setting, we first define the Kullback-Leibler risk, in order to place the problem within a decision the…
Study proposes hybrid machine learning models for crop yield prediction.
problem Crop yield prediction for food security.
method Hybrid machine learning models (ANN-ICA and ANN-GWO).
result ANN-GWO model outperformed ANN-ICA in crop yield prediction.
missForestPredict fills missing data for prediction models quickly and accurately.
problem Missing data in input variables for prediction models.
method Iterative imputation using random forests until convergence.
result missForestPredict outperforms other imputation methods in prediction settings.
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-o…
Classification with Reject Option: Distribution-free Error Guarantees via Conformal Predictionstat.ML
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
Efficiently combines probabilistic predictions using kernel embeddings.
problem Combining multiple probabilistic predictions to improve forecast accuracy.
method Embed predictions into RKHS, optimizes kernel-based scoring rules, finds efficient implementation.
result Generalised linear pool outperforms traditional linear pool in operational wind speed forecasts.
Develops a category-theoretic approach to interpret conformal prediction.
problem Interpreting conformal prediction as a quantitative uncertainty tool.
method Category-theoretic approach to represent and decompose conformal prediction.
result Decomposes conformal prediction into two steps: predictive distributions and prediction regions.
Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of th…
Paper uses machine learning for stock prediction using fundamental data.
problem Predicting stock prices using fundamental data.
method Used three machine learning algorithms (FNN, RF, ANFIS) and feature selection for stock prediction.
result Random Forest (RF) model achieved the best prediction results.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forestsstat.ML
RFpredInterval package builds prediction intervals for random forests and boosted forests.
problem Quantifying uncertainty in random forest and boosted forest point predictions.
method 16 methods to build prediction intervals with random forests and boosted forests.
result The proposed method outperforms existing methods in building prediction intervals.
Separable losses are inconsistent for structured prediction models.
problem Inconsistency of separable losses in structured prediction models.
method Analysis of separable negative log-likelihood losses for structured prediction.
result Separable losses are not Bayes consistent and may not predict the most probable structure.
Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computat…
Validates conformal prediction for network data under non-uniform sampling.
problem Validity of conformal prediction for network data under non-representative sampling.
method Interprets sampling mechanisms as selection rules, studies validity conditional on selection events, uses permutation invariance and joint exchangeability.
result Finite-sample validity of conformal prediction for certain selection events and asymptotic validity for random walk sampling.
We consider a model of selective prediction, where the prediction algorithm is given a data sequence in an online fashion and asked to predict a pre-specified statistic of the upcoming data points. The algorithm is allowed to choose when to make the prediction as well as the length of the prediction window, possibly de…
Split conformal prediction provides finite-sample guarantees for black-box models without distributional assumptions.
problem Weak performance guarantees for modern predictive models under minimal assumptions.
method Develops finite-sample guarantees for split conformal prediction, a method that uses nested prediction sets and order statistics.
result The coverage of prediction sets based on order statistics stochastically dominates the Beta distribution.
Paper predicts stock market values using machine learning.
problem Predicting stock market values for Tehran stock exchange groups.
method Used machine learning algorithms including Decision Tree, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, XGBoost, Artificial neural network, Recurrent Neural Network, and Long short-term memory (LSTM).
result LSTM shows highest accuracy among all algorithms tested.
Improved online classification with accurate predictions.
problem Online classification challenges with limited data.
method Designing an online learner that uses predictions to reduce regret.
result Expected regret is better than worst-case analysis, especially with accurate predictions.
In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced …
Backward Conformal Prediction offers flexible control over prediction set sizes while ensuring coverage guarantees.
problem Providing reliable prediction sets with controlled sizes in applications like medical diagnosis.
method Defines a rule that constrains prediction set sizes based on observed data, adapting coverage levels.
result Maintains computable coverage guarantees while ensuring interpretable, well-controlled prediction set sizes.
A stability-based method selects the most desirable conformal prediction set.
problem Selecting the most desirable conformal prediction set from multiple valid sets invalidates coverage guarantees.
method A stability-based approach that ensures coverage for the selected prediction set.
result The stability-based approach maintains coverage guarantees for the selected prediction set.
Nearly Maximally Predictive Features and Their Dimensionscond-mat.stat-mech
Scientific explanation often requires inferring maximally predictive features from a given data set. Unfortunately, the collection of minimal maximally predictive features for most stochastic processes is uncountably infinite. In such cases, one compromises and instead seeks nearly maximally predictive features. Here, …
Predicts asset return distributions using LSTM and quantile regression.
problem Predicting complex asset return distributions.
method Two-stage approach: quantile prediction using asset-specific features, market data adjustment.
result Significantly outperforms existing models (98% improvement over baseline).
The study improves prediction regions for hierarchical data using a projection step.
problem Improving prediction regions for hierarchical multivariate data.
method Integrating a projection step in the split conformal prediction (SCP) procedure to reduce prediction regions.
result The resulting prediction regions are globally smaller under both joint and component-wise coverage objectives.
In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database …
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our m…