New methods for assessing and visualizing feature groups in machine learning models.
problem Lack of methods for interpreting feature groups in machine learning models.
method Permutation-based, refitting, and Shapley-based techniques for grouped feature importance. Introduced a sequential procedure for identifying stable feature combinations. Developed a combined features effect plot.
result Effective methods for assessing and visualizing the importance and effect of feature groups in machine learning models.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
A new model improves click-through rate prediction for recommendation systems.
problem Improving accuracy of click-through rate prediction in recommendation systems.
method Combines traditional feature engineering with deep neural networks to automate feature combinations.
result The model (FNFM) outperforms current deep learning feature combination models.
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.
Unified framework for calculating Shapley values with correlated features.
problem Inaccurate Shapley values due to multicollinearity.
method Unified framework with matrix formulation and mathematical proof.
result Shapley values become independent of feature correlations.
Simplifies forecast combination by using diversity of out-of-sample forecasts.
problem Estimating optimal weights for forecast combinations is challenging.
method Use out-of-sample forecasts to extract features and calculate weights for forecast combination.
result Achieves superior forecasting performance in point forecasts and prediction intervals.
Efficient skin lesion analysis combines deep CNN and handcrafted features.
problem Skin lesion analysis for melanoma detection.
method Combines deep learning and handcrafted features, introduces hybrid features.
result 0.841 score on validation dataset using SVM classifier.
Noisy Feature Mixup improves model robustness with noise-perturbed convex combinations.
problem Improving model robustness against data perturbations.
method Noise-perturbed convex combinations of pairs of data points in input and feature space.
result Improved model robustness and favorable trade-offs between accuracy and robustness.
This paper makes kernels interpretable for wide feature matrices.
problem Loss of interpretability in kernel methods for wide feature matrices.
method Re-express kernel solution as a linear combination of original features with a special metric.
result Interpretable kernel solutions for wide feature matrices are possible.
Combines static and dynamic features for better multivariate sequence classification.
problem Improving classification performance using both static and dynamic features.
method Generative models (HMM and LSTM) extract temporal information from dynamic data. Static features are combined with the extracted temporal information.
result Hybrid approach outperforms other methods on public datasets.
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.
Combines multiple data types to predict emotions in images.
problem Predicting emotions in images using various data types.
method Combines facial features, scene extraction, audio tonality, human pose, text-based tagging, and CNN predictions.
result Improves accuracy in emotion prediction compared to baseline methods.
EFSIS combines feature selection stability and accuracy.
problem Improving feature selection stability and accuracy in ensemble learning.
method Combines data perturbation and function perturbation strategies.
result Empirical results show high prediction accuracy and stability.
A novel approach combines feature importance scores with deep learning for forex price prediction.
problem Improving forex price prediction using deep learning models.
method Feature importance recap combined with stacking models.
result Proper feature selection significantly improves model performance.
Combining distributed Gaussian Processes with deep CNNs improves performance on action recognition.
problem Improving performance on action recognition datasets.
method Combining distributed Gaussian Processes with multi-stream deep CNNs, treating each CNN as an expert and combining predictions using a Product of Experts (PoE) framework.
result Improves performance on HMDB-51 dataset by 0.4\% compared to hand-crafted feature frameworks.
AFN learns adaptive-order feature interactions for better predictive models.
problem Learning optimal feature interactions in predictive models.
method AFN uses a logarithmic transformation layer to learn arbitrary-order cross features adaptively.
result AFN outperforms state-of-the-art models on four real datasets.
FiBiNET combines feature importance and bilinear interactions for CTR prediction.
problem Improving click-through rate prediction in advertising and feed ranking systems.
method FiBiNET dynamically learns feature importance via SENET and bilinear feature interactions.
result FiBiNET outperforms shallow and deep models on real-world datasets.
Speech emotion recognition system using features and text.
problem Improving accuracy in emotion recognition from speech.
method Used speech features (Spectrogram, MFCC) and text, trained Deep Neural Networks.
result Combined MFCC-Text CNN model achieved highest accuracy.
We present a novel, log-radius profile representation for convex curves and define a new operation for combining the shape features of curves. Unlike the standard, angle profile-based methods, this operation accurately combines the shape features in a visually intuitive manner. This method have implications in shape an…
Improved object classification using neural networks with known and unknown features.
problem Improving classification accuracy for objects described by both known and unknown features.
method Modernized Informational Neurobayesian Approach with consideration of unknown features.
result The method completely solved the problem of misclassification for queries with combining known and unknown features.
Interpretable classifier improves accuracy through probability series expansion.
problem Improving classifier accuracy while maintaining interpretability.
method Directly measures class probabilities from training data, refines predictions through series expansion.
result Achieves comparable accuracy to Random Forests on four datasets.
GMLP learns feature groups for tabular data without known structure.
problem Deep learning for tabular data with unknown feature interactions.
method Group-wise operations and sparse feature grouping matrix learned through temperature annealing softmax.
result GMLP achieves state-of-the-art classification performance on various datasets.
Optimized fuzzy entropy framework improves feature selection and classification performance.
problem Improving feature selection and classification in fuzzy entropy frameworks.
method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.
result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.
Paper tackles non-identifiability in latent feature models, proposing a novel method.
problem Non-identifiability in latent feature models.
method A novel method for parameter estimation that solves non-identifiability.
result The method can find an appropriate solution by hopping efficiently through equivalent solutions.
Radiomics models improved by combining features from multiple modalities and classifiers.
problem Reduced predictive performance from combining features from a single modality and challenges in selecting optimal classifiers.
method Developed a reliable classifier fusion strategy using modality-specific classifiers and an analytic evidential reasoning (ER) rule.
result ER rule-based radiomics models outperformed traditional models.
Study predicts stock mid-prices using machine learning with selected features.
problem Predicting mid-price movements in stock markets.
method Wrapper feature selection using entropy, LMS, LDA, and adaptive logistic regression.
result Best performance achieved with a small set of advanced features.
Paper combines LSTM and XGB for better hypoxemia prediction.
problem Forecasting hypoxemia in operating rooms using time series data.
method Feature learning via LSTM networks and prediction via gradient boosting trees.
result Augmenting XGB with LSTM features improves prediction performance.
MCLNN improves audio classification with binary masks.
problem Improving audio classification accuracy.
method Binary mask applied to CLNN for feature preservation and combination exploration.
result Competitive recognition accuracies on GTZAN and ISMIR2004 datasets.
IUS framework predicts EUR/USD exchange rate with improved accuracy.
problem Accurate forecasting of EUR/USD exchange rate.
method Combines large language models for sentiment analysis, deep learning for forecasting, and feature selection.
result Optuna-optimized Bi-LSTM model reduces MAE and RMSE by 10.69% and 9.56% respectively.
Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detectin…
TGNN combines GNN and SMM for better trading network predictions.
problem Predicting asset prices in trading networks with structural impact factors.
method Combines GNN and SMM for asset price prediction.
result TGNN outperforms existing methods in prediction accuracy.
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vec…
Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more…
A two-layer classifier improves smartphone transportation mode recognition.
problem Improving accuracy of transportation mode classification.
method Two-layer hierarchical classifier combining time and frequency domain features.
result Maximum classification accuracy of 97.02%.
Combines datasets to improve model fitting with small sample sizes.
problem Improving model performance with limited samples from at least one dataset.
method Proposes a novel framework called Combine datasets based on Imputation (ComImp) and PCA-ComImp for combining datasets with missing data.
result Significant improvement in model accuracy, especially with small datasets and when combined with transfer learning.
CTRF combines logged data and randomized experiments for robust prediction.
problem Robust prediction models to handle distributional shifts between training and testing data.
method CTRF uses existing training data and a small amount of randomized experiment data to train a robust model.
result CTRF produces robust predictions and outperforms baseline methods in the presence of feature shifts.
The paper improves prediction and testing for signals from a linear combination of translated features with Gaussian noise.
problem Predicting and testing signals from a linear combination of translated features with varying scale parameter and Gaussian noise.
method Extends previous off-the-grid prediction results, improves minimal distance between features, proposes a goodness-of-fit test with upper bounds.
result Upper bounds on the minimax separation rate match those for the high-dimensional linear model, matching the lower bound.
New method reconstructs missing variables in time series using autoencoders and automatic differentiation.
problem Reconstruct missing variables in time series with flexible input and output combinations.
method Train an autoencoder with all features, optimize missing variables as inputs, and use automatic differentiation.
result Flexible input and output combinations can be achieved without retraining the autoencoder.
Cross-Modulation Networks improve few-shot learning by combining information at multiple levels.
problem Few-shot learning challenges in deep feature extraction.
method Integrates support and query examples at various levels of abstraction using a feature-wise modulation mechanism.
result Encouraging initial results on miniImageNet, closing the gap with state-of-the-art.
A new framework selects best outlier detectors locally for improved ensemble performance.
problem Challenges in combining outlier detectors without ground truth.
method Locally Selective Combination in Parallel Outlier Ensembles (LSCP) framework.
result LSCP_AOM variant consistently outperforms other methods on real-world datasets.
An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conven…
New method selects features using graph-based interactions and elastic net.
problem Loss of feature relationships in vector-based feature selection.
method Graph-based structurally interacting elastic net method.
result Preserves feature relationships and selects informative features.
Improves signature verification accuracy using deep CNN features.
problem Handwritten signature verification accuracy.
method Deep CNN features combined with writer-independent SVM classifier.
result Proposed approach outperforms other WI-HSV methods.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
problem Expanding and reducing feature space in tabular learning becomes computationally expensive with increased dimensionality.
method SCOPE-FE controls the search space by regulating operator and feature-pair spaces, using OperatorProbing and FeatureClustering.
result SCOPE-FE reduces feature engineering time while maintaining competitive predictive performance.
New CNN learns depth features from scratch, outperforming transfer learning.
problem Limited depth data for RGB-D scene recognition.
method Bottom-up approach combining local weakly supervised training and global fine-tuning, modified CNN architecture.
result State-of-the-art accuracy on NYU2 and SUN RGB-D datasets.
A new volatility model blends large and small volatility features.
problem Modeling stock returns and realized volatility.
method Combines multiplicative and Heston models for volatility.
result Steady-state distribution is power-law at both large and small volatilities.
Paper uses ML to predict stock price movements from order book data.
problem Forecasting stock price movements in financial markets.
method Combines handcrafted and ML features for three classifiers, evaluated on two setups.
result Machine Learning shows promise for this task, suggesting future research.
ALICE combines feature selection and inter-rater agreeability for ML model insights.
problem Improving interpretability of black box machine learning models.
method Integrates feature selection and inter-rater agreeability into a user-friendly Python library.
result Initial experiments on customer churn modeling show promising insights.