feature selection
549 papers with code • 0 benchmarks • 1 datasets
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Latest papers
PEACH: Pretrained-embedding Explanation Across Contextual and Hierarchical Structure
In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner.
ALICE: Combining Feature Selection and Inter-Rater Agreeability for Machine Learning Insights
This paper presents a new Python library called Automated Learning for Insightful Comparison and Evaluation (ALICE), which merges conventional feature selection and the concept of inter-rater agreeability in a simple, user-friendly manner to seek insights into black box Machine Learning models.
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.
The CAST package for training and assessment of spatial prediction models in R
One key task in environmental science is to map environmental variables continuously in space or even in space and time.
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.
