Fuzzy trees and forests—Review

Sosnowski, Zenon A.; Gadomer, Łukasz

doi:10.1002/widm.1316

Cited by 9 publications

(2 citation statements)

References 54 publications

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“…To talk about random forests, we must first talk about decision trees [19], [20]. A decision tree is a basic classifier that generally divides features into two categories, but decision trees can also be used for regression.…”

Section: Random Forestmentioning

confidence: 99%

A review of the application of machine learning technologies in vehicle navigation and positioning

Zheng

et al. 2022

Second International Conference on Optics and Communication Technology (ICOCT 2022)

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In recent years, the accuracy requirement of vehicle navigation and positioning is higher and higher. Since some obvious disadvantages emerge in the integration of various traditional technologies, many studies have begun to apply machine learning to vehicle navigation and positioning, which utilize the powerful self-learning ability of machine learning algorithms. The main advantages of machine learning methods include solving the problem of narrow application scope of traditional information fusion algorithms. Solve the problems of low navigation and positioning accuracy and poor anti-interference ability. In this paper, the applications of machine learning related algorithms in vehicle navigation and localization are overviewed in detail, including support vector machines, neural networks and random forests. Meanwhile, the application research status of machine learning technology in vehicle navigation and positioning is summarized, and the future research directions are prospected.

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Section: Random Forestmentioning

confidence: 99%

A review of the application of machine learning technologies in vehicle navigation and positioning

Zheng

et al. 2022

Second International Conference on Optics and Communication Technology (ICOCT 2022)

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“…These are also a few examples which show that this classifier is popular and widely used solution for different kinds of problems. A lot of popular solutions based on fuzzy trees and forests are presented in Sosnowski and Gadomer (2019).…”

Section: C-fuzzy Random Forestmentioning

confidence: 99%

Pruning trees in C-fuzzy random forest

Gadomer

Sosnowski

2020

Soft Comput

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Pruning decision trees is the way to decrease their size in order to reduce classification time and improve (or at least maintain) classification accuracy. In this paper, the idea of applying different pruning methods to C-fuzzy decision trees and Cluster–context fuzzy decision trees in C-fuzzy random forest is presented. C-fuzzy random forest is a classifier which we created and we are improving. This solution is based on fuzzy random forest and uses C-fuzzy decision trees or Cluster–context fuzzy decision trees—depending on the variant. Five pruning methods were adjusted to mentioned kind of trees and examined: Reduced Error Pruning (REP), Pessimistic Error Pruning (PEP), Minimum Error Pruning (MEP), Critical Value Pruning (CVP) and Cost-Complexity Pruning. C-fuzzy random forests with unpruned trees and trees constructed using each of these pruning methods were created. The evaluation of created forests was performed on eleven discrete decision class datasets (forest with C-fuzzy decision trees) and two continuous decision class datasets (forest with Cluster–context fuzzy decision trees). The experiments on eleven different discrete decision class datasets and two continuous decision class datasets were performed to evaluate five implemented pruning methods. Our experiments show that pruning trees in C-fuzzy random forest in general reduce computation time and improve classification accuracy. Generalizing, the best classification accuracy improvement was achieved using CVP for discrete decision class problems and REP for continuous decision class datasets, but for each dataset different pruning methods work well. The method which pruned trees the most was PEP and the fastest one was MEP. However, there is no pruning method which fits the best for all datasets—the pruning method should be chosen individually according to the given problem. There are also situations where it is better to remain trees unpruned.

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