Information Forests

02/07/2012
by   Zhao Yi, et al.
0

We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-conditional distributions in the resulting partitions. The basic idea consists of deferring classification until a measure of "classification confidence" is sufficiently high, and instead breaking down the data so as to maximize this measure. In an alternative interpretation, Information Forests attempt to partition the data into subsets that are "as informative as possible" for the purpose of the task, which is to classify the data. Classification confidence, or informative content of the subsets, is quantified by the Information Divergence. Our approach relates to active learning, semi-supervised learning, mixed generative/discriminative learning.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/24/2020

Notes on Equitable Partition into Matching Forests in Mixed Graphs and into b-branchings in Digraphs

An equitable partition into branchings in a digraph is a partition of th...
research
07/11/2020

Towards Robust Classification with Deep Generative Forests

Decision Trees and Random Forests are among the most widely used machine...
research
11/07/2016

One Class Splitting Criteria for Random Forests

Random Forests (RFs) are strong machine learning tools for classificatio...
research
09/22/2021

Minimax Rates for STIT and Poisson Hyperplane Random Forests

In [12], Mourtada, Gaïffas and Scornet showed that, under proper tuning ...
research
12/03/2014

Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?

In (zhang2014nonlinear,zhang2014nonlinear2), we have viewed machine lear...
research
05/28/2013

Matrices of forests, analysis of networks, and ranking problems

The matrices of spanning rooted forests are studied as a tool for analys...

Please sign up or login with your details

Forgot password? Click here to reset