New algorithm selects variables from large datasets.
problem Automatic selection of variables from large datasets.
method Uses Graphical Models and combines with OLS method.
result Outperforms LASSO method in forecasting models.
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169,181 papers · 148 categories
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48 results for “automatic dataset selection”
ARDA automatically augments datasets for machine learning models.
problem Improving machine learning model performance through data augmentation.
method ARDA combines data searching, joining, and feature selection to augment datasets.
result Training models on ARDA's augmented datasets leads to better performance.
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.
Automates neural network model selection for efficiency.
problem Efficiently choosing neural network architectures and hyper-parameters.
method Modified micro-genetic algorithm for automated model selection.
result AMS finds efficient neural network models for classification and regression.
Auto-CASH uses Deep Q-Network to automatically select machine learning algorithms.
problem Automatic selection of machine learning algorithms and hyperparameters.
method Pre-trained model based on meta-learning using Deep Q-Network.
result Auto-CASH achieves better performance with shorter time compared to classical and state-of-the-art methods.
Paper presents a Transformer model for automatic domain adaptation.
problem Challenges in selecting or designing domain adaptation algorithms.
method Transformer model approximates and selects domain adaptation algorithms.
result Transformers can approximate and automatically select domain adaptation algorithms.
Automates OD model selection via meta-learning.
problem Selecting effective OD models for new datasets.
method Meta-learning approach based on past model performances.
result MetaOD selects models significantly outperforming existing methods.
Deep learning predicts pharmaceutical formulations with high accuracy.
problem Laborious, time-consuming and costly traditional trial-and-error approach in pharmaceutical formulation development.
method Used deep learning for automatic feature extraction, developed automatic dataset selection algorithm, compared with six machine learning methods.
result Deep neural networks achieved accuracies above 80% in predicting pharmaceutical formulations.
New algorithm efficiently learns sparse causal graphs from time series data.
problem Learning sparse causal graphs from time series data efficiently and automatically selecting the number of edges.
method Cyclical coordinate descent algorithm with two non-parametric error metrics for LASSO coefficient selection.
result State-of-the-art performance on simulated and real datasets.
Automatic Gradient Boostingstat.ML
Simplifies AutoML by using gradient boosting alone.
problem Model selection and hyperparameter optimization for real-world applications.
method Gradient boosting as the sole learning algorithm, combined with model-based hyperparameter tuning and feature encoding.
result Achieves comparable results on half of the datasets and best results on two compared to current AutoML projects.
A new clustering method estimates non-linear boundaries and automatically selects the number of clusters.
problem Discriminative clustering with non-linear boundaries and data abnormalities.
method Regularized mutual information objective function with a mixture of Gaussian and uniform distributions.
result Automatic selection of the number of components and estimation of non-linear boundaries.
AutoFIS automatically selects important feature interactions for CTR prediction models.
problem Manual feature interaction design is inefficient and prone to noise.
method Two-stage algorithm: search stage relaxes feature interactions to continuous parameters, re-train stage refines model performance.
result AutoFIS significantly improves CTR and CVR of FM-based models.
SIC detects elbows in error curves automatically.
problem Automatic elbow detection in error curves.
method Spectral information criterion (SIC) extracts geometric features of error curves.
result SIC provides a subset of models with smaller cardinality than total possible models.
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selectionstat.ML
Bayesian TNKMs automatically infer model complexity and feature relevance.
problem Manual tuning of TN rank and feature dimensions is error-prone and computationally expensive.
method Bayesian approach with hierarchical priors on TN factors for automatic rank and feature selection.
result Superior performance in prediction accuracy, uncertainty quantification, interpretability, and scalability.
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
Bayesian non-parametric model selects latent dimensions automatically.
problem Non-linear, sparse latent variable selection.
method Indian buffet process prior, random Fourier approximation, MCMC sampling.
result Superior performance on synthetic, biological, and text datasets.
LBO optimizes model selection for evolving datasets.
problem Model selection for datasets arriving and evolving over time.
method Online, multitask Bayesian optimization exploiting function correlation.
result LBO outperforms standard BO algorithms on real and synthetic data.
Pipeline selects best RL policy from limited data.
problem Selecting best RL policy from small datasets.
method Task- and method-agnostic pipeline using multiple data splits.
result Pipeline produces higher-performing policies.
New method for automatically smoothing GAMs in large datasets.
problem Lack of reliable and fast methods for automatic smoothing in large datasets of GAMs.
method Empirical Bayes approach with an approximate expectation-maximization algorithm involving double Laplace approximation.
result The method achieves state-of-the-art accuracy and is faster than existing methods.
Paper presents an efficient method for selecting machine learning algorithms and hyper-parameters.
problem Efficient selection of machine learning algorithms and hyper-parameters is challenging for large datasets.
method Progressive sampling-based Bayesian optimization
result Significantly reduces search time, classification error rate, and error rate variability.
A recurring problem when building probabilistic latent variable models is regularization and model selection, for instance, the choice of the dimensionality of the latent space. In the context of belief networks with latent variables, this problem has been adressed with Automatic Relevance Determination (ARD) employing…
HyperImpute improves iterative imputation by automatically selecting models and hyperparameters.
problem Imputing missing values in datasets with variable model specifications.
method Generalized iterative imputation framework that adapts and configures models and hyperparameters automatically.
result Demonstrates superior imputation accuracy compared to benchmarks.
ENAS discovers efficient neural architectures quickly and cheaply.
problem Automatic model design and neural architecture search.
method Parameter sharing and policy gradient for subgraph selection.
result ENAS discovers novel architectures achieving state-of-the-art performance.
Contingency Trainingcs.LG
Contingency Training improves classifier accuracy and robustness against irrelevant variables.
problem Feature selection leaves irrelevant variables in high-dimensional datasets, reducing classifier performance.
method Subsampling and creating constraints to find proper feature importance weights.
result Contingency Training outperforms traditional training methods, especially with irrelevant variables.
This study enhances sales forecasts by integrating market indicators into forecasting models.
problem Traditional forecasting models rely solely on historical demand data.
method Automated integration of macroeconomic time series data (GDP growth) into forecasting models using feature selection methods.
result Feature selection methods, especially Forward Feature Selection, significantly improve forecasting accuracy.
Automatically assesses the quality of online health articles.
problem Lack of automated tools to evaluate the quality of online health information.
method Data mining approach using 10 quality criteria and feature selection.
result Classifier achieved 84%-90% accuracy on 10 criteria.
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to ha…
GP-ALPS automatically selects latent processes for multi-output GPs.
problem Manual selection of latent processes in multi-output GPs is time-consuming and prone to biases.
method Developed a variational inference scheme to automatically choose latent processes.
result Demonstrated suitability of GP-ALPS in preliminary experiments.
A new knot selection method for GAMs reduces model complexity.
problem Choosing optimal knots for B-spline regression in GAMs.
method Adaptive splines combined with Fellner-Schall tuning for automatic knot selection.
result Comparable performance with P-splines but using fewer knots.
Proposes a channel pruning method using attention statistics for deep networks.
problem Manual setting of compression ratios in each layer for deep neural networks.
method Channel pruning based on attention statistics with automatic selection of compression ratio.
result Improved performance in terms of accuracy and computational costs compared to conventional methods.
Scale-adaptive and robust intrinsic dimension estimation via optimal neighbourhood identificationstat.ML
This work introduces a protocol to automatically select the correct range of scales for meaningful Intrinsic Dimension estimation.
problem The Intrinsic Dimension (ID) varies with scale in real-world datasets, leading to erroneous results.
method The protocol selects the correct range of scales by ensuring constant density of data points.
result The method provides a robust and scale-adaptive approach to estimating meaningful Intrinsic Dimension.
MARCO-GE selects clustering algorithms using graph embeddings.
problem Automated selection of clustering algorithms for unseen datasets.
method Transform datasets into graphs, extract latent representations, train a ranking meta-model.
result MARCO-GE outperforms state-of-the-art approaches in algorithm recommendation.
Paper proposes a new method to automatically select Gaussian kernel bandwidth for SVDD.
problem Selecting optimal Gaussian kernel bandwidth for SVDD is crucial but challenging.
method Automatic unsupervised method for selecting Gaussian kernel bandwidth.
result The selected bandwidth is competitive with existing methods and can be computed quickly.
A new Tabu Search method optimizes neural network architecture.
problem Optimizing the architecture of deep neural networks.
method Combines Tabu Search and GDM for automatic architecture selection.
result Demonstrates Tabu Search can automatically select optimal neural network architecture.
New methods rank variables for Gaussian processes better than automatic relevance determination.
problem Variable selection for Gaussian process models using inverse length-scale parameters has limitations.
method Two novel methods rank variables based on their predictive relevance using posterior predictive distribution predictions.
result Improved variable selection compared to automatic relevance determination in terms of variability and predictive performance.
GOLS-I automatically determines learning rates for various neural network training algorithms.
problem Adapting learning rates in stochastic training algorithms for neural networks.
method Gradient-Only Line Search (GOLS-I) for automatically setting learning rates.
result GOLS-I learning rate schedules are competitive with manually tuned rates across multiple algorithms, architectures, datasets, and loss functions.
New method debiases selection bias in PU classification with exposure data.
problem Binary classification from positive and unlabeled data with selection bias.
method Automatic Debiased PUE (ADPUE) learning method.
result ADPUE outperforms traditional PU learning methods on various datasets.
Automatically identifies RRLyrae stars from VVV survey data.
problem Classifying RRLyrae stars from a large dataset of light curves.
method Developed an automatic ML-based procedure to identify RRLs, using features like period and intensity, and pseudo-colors.
result Constructed an ensemble classifier with Recall of 0.48 and Precision of 0.86 over 15 tiles.
AutoGMM automates Gaussian mixture modeling in Python.
problem Automatic clustering of complex data with uncertainty-aware grouping.
method Strategic initialization using an agglomerative Mahalanobis heuristic, parallelized model selection by information criteria.
result Strong out-of-the-box performance on classic benchmarks and real datasets.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
problem Determining optimal decomposition ranks in tensor decompositions.
method MARS uses binary masks to learn optimal tensor structure during training via relaxed MAP estimation.
result MARS achieves better results than previous methods in various tasks.
Proposes a model selection method for t-SNE perplexity.
problem Manual tuning of t-SNE perplexity is difficult and requires expertise.
method Develops a model selection objective for t-SNE perplexity that minimizes extra computation.
result Perplexity settings found by the approach are consistent with human expert preferences.
Proposes learning default hyperparameters from empirical results.
problem Selecting optimal hyperparameters for machine learning algorithms.
method Learning a set of complementary default values from a database of prior empirical results.
result Learning default values improves performance and is more efficient than random search and Bayesian Optimization.
A new framework tackles CASH problem with alternating optimization and Rising Bandits.
problem Efficiently solving the Combined Algorithm Selection and Hyperparameter optimization (CASH) problem.
method Alternating optimization framework using BO for HPO and Rising Bandits for algorithm selection.
result Demonstrated superiority over competitive baselines in extensive experiments.
SIAN bridges simple models to neural networks by identifying necessary feature combinations.
problem The gap between simple models and powerful neural networks in performance.
method Feature interaction detection and sparse selection algorithm.
result Competitive performance across multiple tabular datasets with optimal tradeoff.
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and com…
The paper improves ASR accuracy using semi-supervised learning and dropout.
problem Improving ASR accuracy with limited labeled data.
method Training a seed model on limited labeled data, using dropout for uncertainty, and data selection for diversity.
result The approach significantly reduces ASR errors compared to baseline.
New approach for active learning in overparameterized models.
problem Efficiently labeling datasets in machine learning.
method MaxiMin Active Learning for nonparametric or overparameterized models.
result Automatically identifies decision boundaries and data clusters.
Bayesian approach controls FDR in high-dimensional models.
problem High-dimensional variable selection and inference.
method Adapted Mirror Statistic to Bayesian framework for FDR control.
result Effective FDR control without data splitting.