The paper introduces a framework to select efficient datasets for preserving model rankings.
problem Efficient evaluation of machine learning models on small, representative datasets.
method Bootstrap aggregation, clustering, design criteria, random baselines, and greedy farthest-first (FAFI).
result Several selection strategies improve rank preservation compared to random subsets, especially in time series classification.
A new method selects the best feature selection technique for datasets.
problem Selecting the best feature selection method for unseen datasets.
method Data synthesis, meta features, fuzzy similarity, classification model training.
result Successfully recommended the best feature selection method for five out of eight datasets.
Paper evaluates and improves private feature selection methods.
problem Feature selection in high-dimensional datasets with privacy constraints.
method Correlations-based order statistic privatized for feature selection.
result Our method significantly outperforms established baseline for private feature selection.
Random small feature subsets outperform FS in diverse datasets.
problem The significance of selected features in high-dimensional datasets is questionable.
method Analysis of 28 diverse datasets (microarray, RNA-Seq, etc.).
result Any arbitrary set of features performs as well as or better than selected features across datasets.
LLMs can be influenced by unseen dataset subtexts, revealing new ways to select data subsets.
problem Understanding how datasets subtly influence LLMs and their properties.
method Logit-Linear-Selection (LLS) method to select subsets of datasets.
result LLS reveals hidden effects in LLMs that persist across different models and architectures.
This work improves Gaussian process model selection for large datasets.
problem Prohibitively high computational cost in Gaussian process model selection.
method Linear-time scaling and computational uncertainty tradeoff.
result Computation-aware Gaussian processes can be trained on large datasets efficiently.
BWS selects best window subsets for efficient data pruning.
problem Challenges in selecting subsets of large datasets for neural network training.
method Best Window Selection (BWS) by choosing optimal window intervals from ordered sample scores.
result BWS outperforms other methods across various selection ratios and datasets.
DsDm selects data to improve model performance, avoiding handpicked notions of quality.
problem Selecting data for model training can lead to worse performance than random selection.
method Formulates dataset selection as an optimization problem, maximizing model performance.
result Selected datasets improve language model performance by 2x over baseline methods.
Anisotropic neural network selects relevant features from datasets.
problem Reduction of irrelevant features improves model interpretability and performance.
method General Regression Neural Network with an anisotropic Gaussian Kernel.
result The method robustly selects features from simulated and real-world datasets.
A novel weighted feature selection method using fuzzy sets improves classification accuracy and stability.
problem Improving feature selection accuracy and stability in machine learning models.
method Combination of four feature selection methods using fuzzy sets and bootstrap.
result Our method achieved significantly higher stability than individual methods.
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.
MTL method uses unlabeled data with pseudo labels to improve classification with disjoint datasets.
problem Improving classification performance with disjoint labeled datasets using unlabeled data.
method Proposes MTL-SA method to select and augment unlabeled data with confident pseudo labels and close distribution to labeled data.
result Extensive experiments show the effectiveness of MTL-SA method in improving classification performance.
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.
Value selection reduces model size while maintaining accuracy.
problem Space efficiency in model size reduction.
method Two probabilistic methods based on information theory's metric: PVS and P + VS.
result Value selection achieves balance between accuracy and model size reduction.
Feature selection from wide datasets leads to misleading results.
problem Feature selection in wide datasets with few samples can lead to misleading results.
method Derived sample size requirement for declaring features different, used real datasets to illustrate issues.
result Feature selection from very wide datasets may lead to misleading results.
MIBoost boosts variable selection with multiple imputation.
problem Missing data complicates variable selection in statistical models.
method Gradient boosting with multiple imputation, MIBoost.
result MIBoost yields comparable predictive performance to other methods.
A method to select validation data from a dataset using statistical criteria.
problem Selecting a validation basis from a full dataset for machine learning model validation.
method Adopting a 'design of experiments' point of view and using statistical criteria, particularly Maximum Mean Discrepancy criteria.
result The 'support points' concept is particularly relevant for selecting validation data.
Exact selective inference with randomization for Gaussian regression models.
problem Exact selective inference in Gaussian regression models.
method Introduces a pivot for exact selective inference with randomization, reducing the problem to a bivariate truncated Gaussian distribution.
result Our pivot leads to exact inference and produces narrower confidence intervals than related methods.
Develops a faster model selection method using influence functions.
problem Time-consuming and computationally inefficient model selection.
method Model-specific data subsampling guided by influence functions.
result Empirically shows improved selection of high-quality models.
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation…
New unsupervised feature selection method for imbalanced datasets.
problem Feature selection challenges in imbalanced multi-class datasets.
method Distance Rank Score using Spearman's Rank Correlation.
result Outperforms existing methods on clustering problems.
PEAKS selects key training examples incrementally based on prediction error and kernel similarity.
problem Dynamic data selection in deep learning models.
method Prediction Error Anchored by Kernel Similarity (PEAKS) for incremental data selection.
result PEAKS outperforms existing selection strategies and yields better performance returns as training data size grows.
This paper reviews feature selection methods using swarm intelligence.
problem Reducing dimensionality in high-dimensional datasets.
method Comparative analysis of swarm intelligence-based feature selection methods.
result Evaluation of strengths and weaknesses of swarm intelligence-based methods.
Proposes mCS for multivariate selection with FDR control.
problem Selecting high-quality candidates from multivariate datasets.
method Introduces regional monotonicity and multivariate nonconformity scores.
result Significantly improves selection power with FDR control.
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-sc…
As known, attribute selection is a method that is used before the classification of data mining. In this study, a new data set has been created by using attributes expressing overall satisfaction in Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking search method using…
SYNTHONY selects tabular synthesizers based on stress profiling and user intent.
problem Non-uniform performance of tabular generative models across datasets.
method Stress profiling and intent-conditioned tabular synthesis selection.
result Meta-features predict synthesizer performance, improving selection accuracy.
Transfer learning improves prediction quality in high-dimensional sparse regression.
problem Selecting hyperparameters for Lasso-based transfer learning algorithms.
method Asymptotic analysis using the replica method.
result Ignoring one type of transferred information has minimal impact on performance.
Study investigates how preprocessing, feature selection, and model selection affect performance on imbalanced genetic data.
problem Challenges in using machine learning on imbalanced genetic datasets.
method Comparative analysis of data preprocessing, feature selection techniques, and machine learning models on imbalanced genetic data.
result Class-imbalanced target variables and skewed predictors have little to no impact on classification performance.
A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
problem Selecting models for ensemble learning to improve performance and reduce correlation issues.
method Formulate budgeted ensemble selection as maximizing mutual information, use Gaussian-copula to model correlated errors, propose a greedy mutual-information selection algorithm.
result Our method consistently outperforms strong baselines across multiple datasets.
RENT selects stable features for robust model interpretation.
problem Stability in feature selection for robust model interpretation.
method RENT uses an ensemble of elastic net models trained on distinct subsets of data.
result RENT selects features with high stability improving model robustness.
Bayesian model selection improves causal discovery in complex datasets.
problem Identifying causal direction in Markov equivalence classes with realistic assumptions.
method Incorporating causal assumptions within Bayesian framework for model selection.
result Bayesian model selection outperforms previous methods on various datasets.
Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper pr…
Time series forecasting models fail to consistently select the best model across different datasets.
problem Inconsistency in model selection for time series forecasting across varying data regimes.
method Characterized time series using descriptors like trend strength, seasonality, noise level, and temporal dependence. Developed a rule-based selection mechanism to map data regimes to candidate models.
result Rule-based model selection achieves low accuracy, with correct model identification occurring in only a small fraction of cases.
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizi…
Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout modern society. Unfortunately, the explosion in the size and dimensionality of real-world datasets poses a severe challenge to standard featur…
fastHDMI improves neuroimaging variable selection in high-dimensional data.
problem Efficient variable screening in high-dimensional neuroimaging datasets.
method Three mutual information estimation methods implemented in fastHDMI.
result FFTKDE-based method superior for continuous nonlinear outcomes.
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.
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and selec…
We propose an online method for concept driftdetection based on dynamic classifier ensemble selection. Theproposed method generates a pool of ensembles by promotingdiversity among classifier members and chooses expert ensemblesaccording to global prequential accuracy values. Unlike currentdynamic ensemble selection app…
Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features. We give empirical evidence for this problem in real-world medical settings using the state-of-the-art model DeepHit. Furthe…
A new method uses vector embeddings to improve analytics model performance.
problem Challenges in selecting high-quality datasets for enhanced analytics performance.
method Transform datasets into vector embeddings using NumTabData2Vec, then use similarity search for model inference.
result The proposed method accurately predicts analytics outcomes and increases speedup.
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.
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.
Study compares mutation validation and cross-validation for model selection.
problem Comparing model selection methods for generalization performance and computational efficiency.
method Empirical comparison using benchmark and real-world datasets with Bayesian tests.
result Both methods select models with equivalent generalization performance but MV selects simpler models and is computationally cheaper.
A stability metric compares feature selection algorithms in machine learning.
problem Stability of feature selection algorithms in machine learning.
method Rank-based instability index to compare MDA, LIME, and SHAP algorithms.
result LIME and SHAP are more stable than MDA, with LIME being best for human interpretability.
Datasets with hundreds to tens of thousands features is the new norm. Feature selection constitutes a central problem in machine learning, where the aim is to derive a representative set of features from which to construct a classification (or prediction) model for a specific task. Our experimental study involves micro…
We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: (i) \emph{leverage scores-based} and (ii) \emph{norm-based} feature selection. Experimenta…