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Identifying Feature Relevance using a Random Forest
Published on 2007-02-2512624 Views
Many feature selection algorithms are limited in that they attempt to identify relevant feature subsets by examining the features individually. This paper introduces a technique for determining featur
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Presentation
Identifying Feature Relevance Using a Random Forest00:00
Overview00:19
Random Forest01:17
Random Forest (cont...)01:46
Feature Relevance: Ranking03:05
Feature Relevance: Subset Methods04:19
Relevance Identification using Average Information Gain05:56
Node Complexity Compensation07:18
Unique & Non-Unique Arrangements08:01
Node Complexity Compensation (cont…)08:49
Information Gain Density Functions09:43
Employing Feature Relevance11:25
Parallel12:51
Convergence Rates14:08
Results15:03
Irrelevant Features16:19
Expected Information Gain16:56
Bounds on Expected Information Gain18:56
Irrelevant Features: Bounds19:39
Friedman20:13
Simple21:42
Summary23:49