feature selection
539 papers with code • 0 benchmarks • 1 datasets
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Use these libraries to find feature selection models and implementationsLatest papers with no code
Automated Feature Selection for Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations.
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging.
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively.
Automated data processing and feature engineering for deep learning and big data applications: a survey
In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning
Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining.
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.
Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches
This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques.
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional temporal dimension, often exhibit impractical computational complexities with high dimensional data.
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
In this paper, we introduce the problem of feature \emph{reselection}, so that features can be selected with respect to secondary model performance characteristics efficiently even after a feature selection process has been done with respect to a primary objective.