deep forest

Interpreting Deep Forest through Feature Contribution and MDI Feature Importance
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Yi-Xiao He, Shen-Huan Lyu, Yuan Jiang
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Interaction Representations Based Deep Forest Method in Multi-Label Learning
This paper presents an interaction-based representation method for multi-label deep forests.
Shen-Huan Lyu, Yi-He Chen, Yuan Jiang
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Depth is More Powerful than Width
This is an oral representation related to the paper “Depth is More Powerful than Width with Prediction Concatenation in Deep Forest”.
Dec 8, 2022 1:00 PM — 3:00 PM New Orleans Convention Center, Online
Lyu Shen-Huan
Depth is More Powerful than Width
Depth is More Powerful than Width with Prediction Concatenation in Deep Forests
This paper presents a consistency theory for deep forests.
Shen-Huan Lyu, Yi-Xiao He, Zhi-Hua Zhou
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A Region-Based Analysis for the Feature Concatenation in Deep Forests
This paper presents a region-based analysis for the feature concateantion in deep forests.
Shen-Huan Lyu, Yi-He Chen, Zhi-Hua Zhou
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