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Feature Selection

128 papers with code · Computer Code

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Dynamic Refinement Network for Oriented and Densely Packed Object Detection

20 May 2020Anymake/DRN_CVPR2020

However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.

FEATURE SELECTION OBJECT DETECTION

34
20 May 2020

Ensemble Wrapper Subsampling for Deep Modulation Classification

10 May 2020dl4amc/dds

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.

FEATURE SELECTION

0
10 May 2020

A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification

21 Apr 2020groupoasys/Medical_data

In recent years, feature selection has become a challenging problem in several machine learning fields, particularly in classification problems.

FEATURE SELECTION

2
21 Apr 2020

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

30 Mar 2020uzh-rpg/vilib

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

79
30 Mar 2020

Selecting Relevant Features from a Universal Representation for Few-shot Classification

20 Mar 2020dvornikita/SUR

Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples.

FEATURE SELECTION FEW-SHOT LEARNING

14
20 Mar 2020

High-Dimensional Feature Selection for Genomic Datasets

27 Feb 2020majid1292/DRPT

In the presence of large dimensional datasets that contain many irrelevant features (variables), dimensionality reduction algorithms have proven to be useful in removing features with low variance and combine features with high correlation.

DIMENSIONALITY REDUCTION FEATURE SELECTION

0
27 Feb 2020

Cyber Attack Detection thanks to Machine Learning Algorithms

17 Jan 2020antoinedelplace/Cyberattack-Detection

The Random Forest Classifier succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.

CYBER ATTACK DETECTION FEATURE SELECTION INTRUSION DETECTION

8
17 Jan 2020

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

17 Jan 2020WilliamCCHuang/GraphLIME

In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.

FEATURE SELECTION

0
17 Jan 2020

A Real-time Global Inference Network for One-stage Referring Expression Comprehension

7 Dec 2019luogen1996/Real-time-Global-Inference-Network

Referring Expression Comprehension (REC) is an emerging research spot in computer vision, which refers to detecting the target region in an image given an text description.

FEATURE SELECTION

3
07 Dec 2019

Efficient Forward Architecture Search

NeurIPS 2019 microsoft/petridishnn

We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers.

FEATURE SELECTION NEURAL ARCHITECTURE SEARCH

105
01 Dec 2019