Table
of Contents Background
Abstract
Basic definition of random forest
Steps of random forest implementation
Principle of differential evolution algorithm
Main parameters of differential algorithm
Flowchart of differential algorithm
Differential algorithm optimization test function code
Complete code includes data download link: https://download.csdn.net/download/ abc991835105/88179071
Based on differential evolution algorithm improved bp neural network mental state assessment
data
matlab programming
effect diagram
result analysis
outlook
back view
Traditional regression analysis generally uses least squares, etc. for fitting, and local optimization capabilities are limited. In this paper, random forests are used for regression analysis, and the enhanced local optimization of each tree is used to better fit the relationship between independent variables and dependent variables. Realize the improvement of regression analysis
Abstract
random forest classification, Tianying algorithm optimization random forest multi-classification, Tianying algorithm mathematical principle
Basic Definition of Random Forest
In machine learning, a random forest is a classifier that contains multiple decision trees, and its output class is determined by the mode of the class output by individual trees. Leo Breiman and Adele Cutler developed algorithms to infer random forests. And "Random Forests" is their trademark. The term comes from random decision forests proposed by Tin Kam Ho of Bell Labs in 1995. This method combines Breimans' "Bootstrap aggregating" idea with Ho's "random subspace method" to build an ensemble of decision trees.
training method
Each tree is constructed according to the following algorithm [1]:
use N to represent the number of training cases (samples), and M to represent the number of features.
The number of input features m is used to determine the