Feature Selection

334 papers with code • 26 benchmarks • 14 datasets

Feature Selection is the process of selecting a subset of the original variables such that a model built on data containing only these features has the best performance. Feature Selection avoids overfitting, improves model performance by getting rid of redundant features and has the added advantage of keeping the original feature representation, thus offering better interpretability.

Source: A comparative study of feature selection methods for stress hotspot classification in materials

Greatest papers with code

TabNet: Attentive Interpretable Tabular Learning

google-research/google-research 20 Aug 2019

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.

Decision Making Feature Selection +3

Feature Selective Anchor-Free Module for Single-Shot Object Detection

open-mmlab/mmdetection CVPR 2019

The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches.

Feature Selection Object Detection

Distributed and parallel time series feature extraction for industrial big data applications

blue-yonder/tsfresh 25 Oct 2016

This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.

Classification Feature Importance +4

X3D: Expanding Architectures for Efficient Video Recognition

facebookresearch/SlowFast CVPR 2020

This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth.

Action Classification Feature Selection +4

Free-Form Image Inpainting with Gated Convolution

JiahuiYu/generative_inpainting ICCV 2019

We present a generative image inpainting system to complete images with free-form mask and guidance.

Feature Selection Image Inpainting

Feature Selection: A Data Perspective

jundongl/scikit-feature 29 Jan 2016

To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).

Feature Selection Sparse Learning

Feature Selection with the Boruta Package

scikit-learn-contrib/boruta_py Journal of Statistical Software 2010 2010

This article describes a R package Boruta, implementing a novel feature selection algorithm for finding all relevant variables.

Feature Selection General Classification

A Light CNN for Deep Face Representation with Noisy Labels

AlfredXiangWu/face_verification_experiment 9 Nov 2015

This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.

Face Identification Face Recognition +3

Fast and accurate sentiment classification using an enhanced Naive Bayes model

vivekn/sentiment 27 May 2013

We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis.

Feature Selection General Classification +1

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

uzh-rpg/vilib 30 Mar 2020

While most steps of a VIO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization.

Feature Selection