典型文献
A Region-Based Analysis for the Feature Concatenation in Deep Forests
文献摘要：
Deep forest is a tree-based deep model made up of non-differentiable modules that are trained without backpropagation.Despite the fact that deep forests have achieved considerable success in a variety of tasks,feature concatenation,as the ingredient for forest representation learning,still lacks theoretical understand-ing.In this paper,we aim to understand the influence of feature concatenation on predictive performance.To en-able such theoretical studies,we present the first math-ematical formula of feature concatenation based on the two-stage structure,which regards the splits along new features and raw features as a region selector and a re-gion classifier respectively.Furthermore,we prove a re-gion-based generalization bound for feature concatenation,which reveals the trade-off between Rademacher com-plexities of the two-stage structure and the fraction of in-stances that are correctly classified in the selected region.As a consequence,we show that compared with the pre-diction-based feature concatenation(PFC),the advant-age of interaction-based feature concatenation(IFC)is that it obtains more abundant regions through distrib-uted representation and alleviates the overfitting risk in local regions.Experiments confirm the correctness of our theoretical results.
文献关键词：
中图分类号：
作者姓名：
LYU Shen-Huan;CHEN Yi-He;ZHOU Zhi-Hua
作者机构：
National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China
文献出处：
引用格式：
[1]LYU Shen-Huan;CHEN Yi-He;ZHOU Zhi-Hua-.A Region-Based Analysis for the Feature Concatenation in Deep Forests)[J].电子学报（英文）,2022(06):1072-1080
A类：
Concatenation,plexities
B类：
Region,Based,Analysis,Feature,Deep,Forests,tree,deep,model,made,up,differentiable,modules,that,trained,without,backpropagation,Despite,fact,forests,have,achieved,considerable,success,variety,tasks,concatenation,ingredient,representation,learning,still,lacks,theoretical,understand,In,this,paper,aim,influence,predictive,performance,To,such,studies,first,math,ematical,formula,two,stage,structure,which,regards,splits,along,new,features,raw,selector,classifier,respectively,Furthermore,prove,generalization,bound,reveals,trade,off,between,Rademacher,fraction,stances,correctly,classified,selected,consequence,show,compared,diction,PFC,advant,interaction,IFC,obtains,abundant,regions,through,distrib,uted,alleviates,overfitting,risk,local,Experiments,confirm,correctness,our,results
AB值：
0.57515
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