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Feature Selection

214 papers with code · Computer Code

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

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Greatest papers with code

TabNet: Attentive Interpretable Tabular Learning

20 Aug 2019google-research/google-research

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

DECISION MAKING FEATURE SELECTION SELF-SUPERVISED LEARNING UNSUPERVISED REPRESENTATION LEARNING

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

CVPR 2019 open-mmlab/mmdetection

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

25 Oct 2016blue-yonder/tsfresh

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.

FEATURE IMPORTANCE FEATURE SELECTION TIME SERIES TIME SERIES CLASSIFICATION

X3D: Expanding Architectures for Efficient Video Recognition

CVPR 2020 facebookresearch/SlowFast

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.

FEATURE SELECTION IMAGE CLASSIFICATION VIDEO CLASSIFICATION VIDEO RECOGNITION

Free-Form Image Inpainting with Gated Convolution

ICCV 2019 JiahuiYu/generative_inpainting

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

29 Jan 2016jundongl/scikit-feature

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

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

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

FEATURE SELECTION

A Light CNN for Deep Face Representation with Noisy Labels

9 Nov 2015AlfredXiangWu/face_verification_experiment

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 FACE VERIFICATION FEATURE SELECTION

Fast and accurate sentiment classification using an enhanced Naive Bayes model

27 May 2013vivekn/sentiment

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

FEATURE SELECTION SENTIMENT ANALYSIS

Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining

22 Nov 2017EpistasisLab/scikit-rebate

Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e. g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e. g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e. g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable.

FEATURE SELECTION