New classification technique: fuzzy oblique decision tree

Abstract

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold δ to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.

Z-number Decision Tree and Its Appliction

“…By fuzzizing data based on tolerance rough fuzzy sets, the efficiency of fuzzification can be improved and the loss of information can be reduced, Yashuang Mu, Lidong Wang and Xiaodong Liu proposed A parallel tree node splitting criterion for fuzzy decision trees [12], they design a parallel tree nodesplitting criterion (MR-NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule, this new method makes the fuzzy decision tree break through the limitation of algorithm complexity and still have good performance on big data, they also designed a fuzzy project division criterion based on dynamic programming under the framework of fuzzy decision tree induction [13]. This fuzzy method changes the defect that the traditional fuzzy decision tree can only perform a fixed number of fuzzy partitions, Yashuang mu, Jiangyong Wang, et al proposed information granulation-based fuzzy partition in decision tree induction [14], in their study, an interval information granulation-based fuzzy partition (InterIG-FP) method is established to define fuzzy items in the framework of fuzzy decision tree induction. This method improves the accuracy and anti noise ability of fuzzy decision tree.…”

Using Intuitionistic Fuzzy Set to Classify Uncertain and Linearly Non-Separable Data

“…A comparison of the system, which is called Polygonal fuzzy weighted (PFW), radial basis function (RBF) and conventional linear kernels in identical experimental conditions showed that it could produce a high rate classification accuracy than these two commonly used kernels with SVM. A fuzzy oblique decision tree (FODT) algorithm is proposed by Cai et al (2019). The proposed algorithm was based on axiomatic fuzzy set (AFS) in which the fuzzy rules are used to construct leaf nodes for each class in each layer of the tree.…”