Abstract: This paper proposed a new decision tree-based ensemble learning method called FL(Forest Learning)．Unlike traditional ensemble learning approaches,such as bagging and boosting,FL directly learns a forest on all training examples as an ensemble rather than on examples obtained by sampling from training set．Unlike the approach of learning ensemble by independently training each classifier and combining them for prediction,FL learns each classifier considering its influence on ensemble performance．FL first employs traditional algorithm to train the first decision tree,and then iteratively constructs new decision trees and add them to forest．When constructing current decision tree,FL considers the influence of each partition on ensemble performance．Experimental results indicate that,compared to traditional ensemble learning methods,FL induces ensemble with much better performance.

Key words: Forest learning,Margin-based theory,Contribution gain,Feature transformation