Feature Selection for Classification: A Review（二）

时间 2020-12-24

标签机器学习繁體版

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1.前言本文主要讨论对flat 特征的特征选择方法，flat feature 即特征被假设为都是独立的特征，主要从三个模型——filter模型，wrapper模型和embeded模型下手进行讨论。 2.filte model 得益于flat特征的特性，filter模型不使用任何分类算法来评价特征，典型的的filter算法分为两步，第一步，将特征根据某一种特定的标准进行排序，特征可以是单变量的也可

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1. Genetic algorithms for feature selection
2. 《Deep learning for time series classification a review》笔记
3. [Feature] Feature selection
4. [Feature] Feature selection - Embedded topic
5. scikit-feature: Open-Source Feature Selection Repo
6. R programming for feature selection and regression
7. A systematic review on regression test selection techniques
8. A Duplication Analysis Based Evolutionary Algorithm for Bi-objective Feature Selection
9. RefineDet + Dynamic Anchor Feature Selection for Single-Shot Object Detection
10. A NOVEL DEEP FEATURE FUSION NETWORK FOR REMOTE SENSING SCENE CLASSIFICATION（论文翻译）

>>更多相关文章<<