Random Fuzzy Granular Decision Tree

Wei Li,Chao Tang,Xiaoyu Ma,Bin Dai,Youmeng Luo,Yumin Chen,Runjing Chen,Kaiqiang Zhang,Venkatesan Rajinikanth

doi:10.1155/2021/5578682

Abstract

In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.

Highlights

When using rough set to generate a decision tree, the main research focus is how to use rough set theory to choose node splitting features. Miao and his colleagues designed a rough set based on multivariate decision tree algorithm. e method first selects the conditional features in the kernel to construct a multivariate test, and generates a new feature to split the node [22]. e advantage of this algorithm is that the training efficiency is relatively high, but because there are too many variables in the nodes of the decision tree, the interpretability of the decision tree is difficult
Aiming at the ordered mutual information decision tree that is widely used in monotonic classification problems, Mu and his colleagues presented a fast version and gave its parallel implementation [40]. ere are other parallel decision tree approaches proposed like distributed fuzzy decision tree [41], parallel Pearson correlation coefficient decision tree [42], etc
A decision tree is constructed in granular space to solve the classification problem. e main contributions are as follows: (i) We propose adaptive global random clustering (AGRC) that can adaptively give the optimal cluster centers, which is a global optimization method and can avoid falling into local optimization solution

Summary

Contributions

A decision tree is constructed in granular space to solve the classification problem. e main contributions are as follows:. A decision tree is constructed in granular space to solve the classification problem. (i) We propose AGRC that can adaptively give the optimal cluster centers, which is a global optimization method and can avoid falling into local optimization solution. (ii) We design the parallel granulation method based on the above clustering algorithm, which solves the problem of high complexity of traditional serial granulation and enhances the granulation efficiency. (iii) In granular space, we define fuzzy granules and related operators and select features based on the information gain ratio to construct a fuzzy granular decision tree for classification. E method presented can solve binary classification or multiclassification problem and give feature importance according to the order of the tree node generated We design the corresponding pruning algorithm. e method presented can solve binary classification or multiclassification problem and give feature importance according to the order of the tree node generated

The Algorithm Description

Findings

Discussion