特定信息增益决策森林分类器研究

Research of decision forest classifier using specific information gain

传统的决策树是利用决策属性的信息增益来进行建模的,而有时决策属性的信息增益是根据属性的不同取值而动态变化的。改进了决策树算法,考虑了决策属性取值不同产生的信息增益的差别。根据决策属性的不同取值创建了基于特定信息增益的决策森林分类模型。实验结果表明虽然决策森林模型的建模过程比决策树复杂,但是具有比较高的分类精度。

Traditional decision tree is based on the information gain of the decision attribute, but sometimes the information gain is changing dynamically according to different values of the decision attribute.This paper considers the differences of the information gain which comes from the different values of the decision attribute and builds the decision forest based on specific information gain.Experiment shows that it has higher classification precision than ID3 though it has a more complex computational procedure.