Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2021.1875866
Machine learning methods based on probabilistic decision tree under the multi-valued preference environment
Wei Zhou
Yi Lu
Man Liu
Keang Zhang
Sažetak
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.
Ključne riječi
Machine learning method; probabilistic decision tree; multi-valued preference; decision making
Hrčak ID:
301674
URI
Datum izdavanja:
31.3.2023.
Posjeta: 160 *