feature selection
549 papers with code • 0 benchmarks • 1 datasets
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Latest papers
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.
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection.
Bridging Domains with Approximately Shared Features
Under our framework, we design and analyze a learning procedure consisting of learning approximately shared feature representation from source tasks and fine-tuning it on the target task.
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
cnulab/realnet
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Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization.
Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent
A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection.
Indirectly Parameterized Concrete Autoencoders
alfred-n/ip-cae
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Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly.
DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
In DeepDRK, a generative model grounded in a transformer architecture is introduced to better achieve the "swap property".
Filter-based multi-task cross-corpus feature learning for speech emotion recognition
In investigating the effectiveness of its proposed method, the present research experiments on eight well-known public speech emotion corpora and compares the results with eight of the best approaches in the literature.
