实验方法是分析决策树算法中的多值偏向问题的传统方法，其缺点是需要具备该数据领域的专家知识。
Experiment is the traditional method for analysing multivalue BIOS of decision tree algorithm, but it has a fault that we must have the expertise of the specific field.
提出了一种避免了多值偏向问题的决策树算法——AF算法；
Second, this paper proposes a new decision tree algorithm, AF algorithm, which avoids multivalue bios.
多值偏向是决策树算法中普遍存在的问题，以往人们对于多值偏向问题的分析主要是基于实验观测的。
Variety bias exists in many decision tree algorithms, and in the past people analyzed this problem mainly based on experiments.
多值偏向可能导致从数据集中归纳出错误的知识，使决策树的性能下降。
Multivalue BIOS may result in inducing wrong knowledge from data set, and consequently result in the decline of the performance of decision tree.
多值偏向可能导致从数据集中归纳出错误的知识，使决策树的性能下降。
Multivalue BIOS may result in inducing wrong knowledge from data set, and consequently result in the decline of the performance of decision tree.
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