feature selection
544 papers with code • 0 benchmarks • 1 datasets
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Latest papers
Quiver Laplacians and Feature Selection
The challenge of selecting the most relevant features of a given dataset arises ubiquitously in data analysis and dimensionality reduction.
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data.
DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
zuowx/deeplink-t
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DeepLINK-T combines deep learning with knockoff inference to control FDR in feature selection for time series models, accommodating a wide variety of feature distributions.
Integrated path stability selection
This yields a tighter bound on E(FP), resulting in a feature selection criterion that has higher sensitivity in practice and is better calibrated in terms of matching the target E(FP).
A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition
On the other hand, the PWFS block employs a feature selection mechanism that discards less meaningful features prior to the fusion process.
Explaining deep learning models for ozone pollution prediction via embedded feature selection
manjimnav/TSLayer-Ozone
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Applied Soft Computing 2024
Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability.
Non-negative Contrastive Learning
pku-ml/non_neg
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In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features.
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
jia-py/erase
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Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.
Iterative Feature Boosting for Explainable Speech Emotion Recognition
In speech emotion recognition (SER), using pre- defined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information.
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
In this paper, we propose IGANN Sparse, a novel machine learning model from the family of generalized additive models, which promotes sparsity through a non-linear feature selection process during training.
