Machine learning methods based on probabilistic decision tree under the multi-valued preference environment

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Ekonomska Istrazivanja-economic Research

سال: 2021

ISSN: ['1848-9664', '1331-677X']

DOI: https://doi.org/10.1080/1331677x.2021.1875866