Using Cluster–Context Fuzzy Decision Trees in Fuzzy Random Forest

Gadomer, Łukasz; Sosnowski, Zenon A.

doi:10.1007/978-3-319-59105-6_16

Cited by 2 publications

(6 citation statements)

References 9 publications

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“…In theory, the number of contexts should respond the number of object groups in the dataset. However, experiments performed in Gadomer and Sosnowski (2017) shown that sometimes it is possible to achieve better results with breaking that assumption. The division into contexts can be performed using any membership function.…”

Section: Cluster-context Fuzzy Decision Treesmentioning

confidence: 99%

“…The detailed description of C-fuzzy random forest with C-fuzzy decision trees was presented in Gadomer and Sosnowski (2016) and Gadomer and Sosnowski (2019). The other variant of C-fuzzy random forest, which uses Cluster-context decision trees, was presented in Gadomer and Sosnowski (2017).…”

Section: C-fuzzy Random Forestmentioning

confidence: 99%

“…The other way of weighting which we proposed uses the estimated quality of trees in the forest. We presented this idea in Gadomer and Sosnowski (2017).…”

Section: Input: X-training Datasetmentioning

confidence: 99%

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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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Section: Cluster-context Fuzzy Decision Treesmentioning

confidence: 99%

Section: C-fuzzy Random Forestmentioning

confidence: 99%

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Pruning trees in C-fuzzy random forest

Gadomer

Sosnowski

2020

Soft Comput

Self Cite

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“…Some comparisons are given and analyzed in the work of Wang et al 8 It not only compares three algorithms but also applies the concept of degree of importance to build a fuzzy decision tree. Currently, the same group then applies clustering in the work of Gadomer and Sosnowski, 27 joining the C-FDT with the approach of context-based fuzzy clustering. 11 Big data is also one of the research branches in the fuzzy decision tree, and lots of novel methods are proposed such as big data-driven smart energy management, 12 indexing techniques, 13 semiautomagical fuzzy partition method, 14 modified Gini index fuzzy SLIQ decision tree algorithm, 15 Chi-FRBCS-BigData algorithm, 16 and multiobjective evolutionary algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…It tests this classifier with four datasets to compare the results and discuss the influence of randomness. Currently, the same group then applies clustering in the work of Gadomer and Sosnowski, joining the C‐FDT with the approach of context‐based fuzzy clustering. Both datasets of discrete and continuous features test this approach and show good results.…”

Section: Related Workmentioning

confidence: 99%

A general model for fuzzy decision tree and fuzzy random forest

Zheng

Zhang

et al. 2018

Computational Intelligence

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The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper.To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS-based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS-based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS-based fuzzy decision tree with linear membership function and nonlinear membership function. KEYWORDSfuzzy decision tree, fuzzy random forest, membership function, risk classification and prediction 310

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Using Cluster–Context Fuzzy Decision Trees in Fuzzy Random Forest

Cited by 2 publications

References 9 publications

Pruning trees in C-fuzzy random forest

Pruning trees in C-fuzzy random forest

A general model for fuzzy decision tree and fuzzy random forest

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