BGRF: A broad granular random forest algorithm

Autor: Xingyu Fu, Yingyue Chen, Jingru Yan, Yumin Chen, Feng Xu
Rok vydání: 2023
Předmět:
Zdroj: Journal of Intelligent & Fuzzy Systems. 44:8103-8117
ISSN: 1875-8967
1064-1246
DOI: 10.3233/jifs-223960
Popis: The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm.
Databáze: OpenAIRE
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