Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input variables i…
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48 results for “Relevance Prediction”
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
Study evaluates relevance metrics for similarity-based model explanations.
problem Providing understandable explanations for complex model predictions.
method Evaluated three relevance metrics using three tests.
result Cosine similarity of gradients performs best for explanations.
New method selects relevant dimensions for better prediction in mixtures.
problem Learning mixtures with limited components for prediction tasks.
method Prediction-focused modeling for mixtures.
result Improves prediction performance compared to non-focused models.
Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.
problem Capturing non-homogeneous data structure with limited kernel functions.
method Uses inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not close to zero. Applies multiple kernel method with different widths. Proposes extended predictive information criterion for scale parameter selection.
result Obtains a multiple kernel relevance vector regression model with good predictive accuracy.
New method explains GNN predictions using walks.
problem GNNs are black-boxes and hard to explain.
method Nested attribution scheme using relevant walks.
result Extracts meaningful explanations from GNNs.
InteractionNet models noncovalent protein-ligand interactions with GNNs and explains predictions.
problem Modeling noncovalent protein-ligand interactions with graph neural networks.
method InteractionNet uses a GNN architecture with separated covalent and noncovalent convolution layers and layer-wise relevance propagation for explainability.
result InteractionNet successfully predicts noncovalent protein-ligand interactions with chemical relevance.
Aiming for Relevancecs.LG
Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
A method to assess variable importance in complex predictive models.
problem Assessing the importance of variables in complex predictive models.
method Assigning relevance measures to each variable by comparing predictions with a ghost variable and analyzing joint effects.
result The method provides insights into variable importance and joint effects not available with other methods.
Paper aims to improve relevance of e-commerce search results.
problem Improving relevance of online product search results.
method Combines machine learning, NLP, and IR techniques to predict relevance scores.
result Deep learning models outperform conventional IR models in relevance prediction.
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, the interpretation of these models itself is of high relevance, and existing approaches therefore enforce e.g. model sparsity. For…
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Setscs.AI
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only availab…
Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process. Depending on the product being purchased, and the organizational structure, the process may involve employees who first conduct market research, and then influence decision makers wh…
Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular…
Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter …
Our interest in this paper is in the construction of symbolic explanations for predictions made by a deep neural network. We will focus attention on deep relational machines (DRMs, first proposed by H. Lodhi). A DRM is a deep network in which the input layer consists of Boolean-valued functions (features) that are defi…
An Automatic Relevance Determination Prior Bayesian Neural Network for Controlled Variable Selectionstat.ML
Bayesian neural network improves feature selection and prediction.
problem Improving feature selection and prediction accuracy in neural networks.
method BNN-ARD with l2-norm feature importance measure.
result Improves variable selection and predictive performance on real-world data.
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specif…
We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimat…
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forestsstat.ML
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifie…
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional n…
Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. In order to optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospit…
Paper introduces MCRP for estimating feature relevance uncertainty in neural networks.
problem Lack of uncertainty in feature relevance for neural network decisions.
method Monte Carlo Relevance Propagation (MCRP) method.
result Allows deeper understanding of neural networks' perception and reasoning.
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an …
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleti…
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them ca…
Proposes a new SPVM model for RVM with more flexible priors.
problem Improper priors on multiple penalty parameters in RVM lead to improper posteriors.
method Introduces a single penalty approach (SPRVM) and a semi-Bayesian fitting method.
result SPRVM allows for more flexible priors and has proven conditions for posterior propriety.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Efficiently computes embeddings for large graphs using coarsening.
problem Inefficient computation of graph embeddings for large-scale graphs.
method Graph coarsening based on Schur complements and Gaussian elimination.
result Efficiently computed embeddings on coarsened graph match Schur complement embeddings in expectation.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
Feature selection with high-dimensional data and a very small proportion of relevant features poses a severe challenge to standard statistical methods. We have developed a new approach (HARVEST) that is straightforward to apply, albeit somewhat computer-intensive. This algorithm can be used to pre-screen a large number…
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the prob…
Proposes a new stability measure for model fitting on similar feature data sets.
problem Model fitting on data sets with similar features is challenging.
method Tuning hyperparameters in a multi-criteria fashion with predictive accuracy and feature selection stability.
result Our approach achieves similar or better predictive performance than single-criteria and stability selection approaches.
Estimates latent dimensionality for prediction tasks using mutual information.
problem Estimating the latent dimensionality needed for accurate prediction.
method Formulates the problem as an Information Bottleneck question and uses neural mutual information estimators with a hybrid critic to preserve latent geometry.
result The hybrid critic method provides a more accurate estimation of task-relevant dimensionality.
Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific …
XR improves search advertising relevance predictions.
problem Accurately predicting relevance of millions of search labels.
method eXtreme Regression (XR) with new evaluation metrics and XReg algorithm.
result XReg outperforms state-of-the-art methods by up to 50% in XR error metric.
Data coarse graining can improve model performancecond-mat.stat-mech
Data coarse graining improves model performance by filtering out less relevant features.
problem Lossy data transformations lose information but can improve model generalization.
method Data coarse graining schemes that systematically discard features based on relevance to the learning task.
result A 'high-pass' scheme helps models generalize better by filtering out less relevant features.
Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input. To do so, our model's prediction relies so…
This paper proposes a cost-sensitive feature acquisition method.
problem Choosing features for prediction with low cost and high accuracy.
method Adaptive feature selection using feature relevance and costs.
result Our approach achieves high accuracy with lower cost than state-of-the-art.
Study proposes explainable analytics for manufacturing process planning.
problem Improving data-driven decision-making in manufacturing.
method Combines process mining, machine learning, and XAI. Uses deep learning for prediction and Shapley values/ICE plots for explanations.
result Enhanced decision-making capabilities through local post-hoc explanations.
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completionphysics.chem-ph
New method predicts activity coefficients for binary mixtures without using physical descriptors.
problem Predicting activity coefficients for unexplored binary mixtures.
method Probabilistic matrix factorization model.
result Method outperforms state-of-the-art models requiring less training effort.
Jointly learns feature and sample relevancies for robust sparse recovery.
problem Sparse recovery sensitivity to data contaminants like outliers or misspecified noise.
method Jointly learns feature and sample relevancies via marginal likelihood optimization.
result Consistent sparse and robust prediction models across diverse tasks.
XGBoost fails to accurately identify relevant features, while interpretable methods do.
problem Accurately identifying relevant features in black-box models like XGBoost.
method Comparison of variable importance methods (CART, Optimal Trees, XGBoost, SHAP) across various experiments.
result Interpretable methods outperform black-box models in feature selection accuracy.
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which dif…
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users speci…
The paper introduces a new method for interpreting model predictions by considering both direct and indirect effects.
problem Interpreting model predictions to understand the causes of decisions.
method Proposes a new approach to quantify feature relevance by combining different types of interpretations and measures.
result Integrates various types of interpretations and measures to provide meaningful insights into model predictions.
Machine learning models predict US economic recessions using Treasury term spreads.
problem Predicting US economic recessions using Treasury term spreads.
method Gradient Boosting and Random Forest methods trained with SHapley Additive exPlanations (SHAP) framework.
result 3 month to 6 month Treasury term spread is the most relevant for predicting US economic recession.
Automatic detection of emotion has the potential to revolutionize mental health and wellbeing. Recent work has been successful in predicting affect from unimodal electrocardiogram (ECG) data. However, to be immediately relevant for real-world applications, physiology-based emotion detection must make use of ubiquitous …