Feature Selection for Classification: A Review（二）
1.前言 本文主要討論對flat 特徵的特徵選擇方法，flat feature 即特徵被假設爲都是獨立的特徵，主要從三個模型——filter模型，wrapper模型和embeded模型下手進行討論。 2.filte model 得益於flat特徵的特性，filter模型不使用任何分類算法來評價特徵，典型的的filter算法分爲兩步，第一步，將特徵根據某一種特定的標準進行排序，特徵可以是單變量的也可
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相關文章
- 1. Genetic algorithms for feature selection
- 2. 《Deep learning for time series classification a review》筆記
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- 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（論文翻譯）