Abstract

In the classification calculation, the data are sometimes not unique and there are different values and probabilities. Then, it is meaningful to develop the appropriate methods to make classification decision. To solve this issue, this paper proposes the machine learning methods based on a probabilistic decision tree (DT) under the multi-valued preference environment and the probabilistic multi-valued preference environment respectively for the different classification aims. First, this paper develops a data pre-processing method to deal with the weight and quantity matching under the multi-valued preference environment. In this method, we use the least common multiple and weight assignments to balance the probability of each preference. Then, based on the training data, this paper introduces the entropy method to further optimize the DT model under the multi-valued preference environment. After that, the corresponding calculation rules and probability classifications are given. In addition, considering the different numbers and probabilities of the preferences, this paper also uses the entropy method to develop the DT model under the probabilistic multi-valued preference environment. Furthermore, the calculation rules and probability classifications are similarly derived. At last, we demonstrate the feasibility of the machine learning methods and the DT models under the above two preference environments based on the illustrated examples.

摘要

在分类计算中，数据有时不是唯一的，存在不同的值和概率。然后，开发适当的方法来进行分类决策是有意义的。针对这一问题，针对不同的分类目标，本文分别提出了多值偏好环境和概率多值偏好环境下基于概率决策树（DT）的机器学习方法。首先，本文开发了一种数据预处理方法来处理多值偏好环境下的重量和数量匹配。在这种方法中，我们使用最小公倍数和权重分配来平衡每个偏好的概率。然后，根据训练数据，本文引入熵方法进一步优化多值偏好环境下的DT模型。然后给出相应的计算规则和概率分类。此外，考虑到偏好的数量和概率不同，本文还利用熵的方法开发了概率多值偏好环境下的DT模型。此外，计算规则和概率分类也是类似推导出来的。最后，我们通过举例说明了机器学习方法和 DT 模型在上述两种偏好环境下的可行性。考虑到偏好的数量和概率不同，本文还利用熵的方法开发了概率多值偏好环境下的DT模型。此外，计算规则和概率分类也是类似推导出来的。最后，我们通过举例说明了机器学习方法和 DT 模型在上述两种偏好环境下的可行性。考虑到偏好的数量和概率不同，本文还利用熵的方法开发了概率多值偏好环境下的DT模型。此外，计算规则和概率分类也是类似推导出来的。最后，我们通过举例说明了机器学习方法和 DT 模型在上述两种偏好环境下的可行性。