| 595 | 11 | 18 |
| Downloads | Citas | Reads |
Decision tree is an efficient and widely used classification algorithm in the field of data mining. Traditional classic decision tree algorithms were difficult to deal with uncertain information. Such as the data with ambiguity. Fuzzy decision tree, as an extension of classic decision tree in fuzzy set theory, could overcome this defect effectively. However, when the existing fuzzy decision tree algorithm was used to processed data with a hierarchical structure of labels, it selected a certain layer of hierarchical structure to classify the data generally. As a result, when the classification accuracy was high, the label was not specific; when the label was specific, the classification accuracy was low. It was impossible to achieve the label as specific as possible effectively when the classification accuracy was as high as possible. A fuzzy decision tree construction algorithm based on hierarchical labels data was proposed to solve the above problems. The algorithm combined the fuzzy ID3 algorithm and the idea of hierarchical information gaining to classify the data, and fully considered the level of the labels in the construction process. Finally, the comparison between the experiment and the traditional fuzzy decision tree algorithm showed the effectiveness of the proposed algorithm.
[1] 廖虎昌，缑迅杰，徐泽水.基于犹豫模糊语言集的决策理论与方法综述[J].系统工程理论与实践，2017,37(1):35-48.LIAO H C,GOU X J,XU Z S.A survey of decision making theory and methodologies of hesitant fuzzy linguistic term set[J].Systems engineering-theory & practice,2017,37(1):35-48.
[2] ZADEH L A.Fuzzy sets[M].Singapore:World Scientific Press,1996:394-432.
[3] ATANOSSOV K.Intuitionistic fuzzy sets[J].International journal bioautomation,2016,20(2):107-115.
[4] 翟俊海，侯少星，王熙照.粗糙模糊决策树归纳算法[J].南京大学学报(自然科学),2016,52(2):306-312.ZHAI J H,HOU S X,WANG X Z.Induction of rough fuzzy decision tree[J].Journal of Nanjing university (natural sciences),2016,52(2):306-312.
[5] HU H W,CHEN Y L,TANG K.A novel decision-tree method for structured continuous-label classification[J].IEEE transactions on cybernetics,2013,43(6):1734-1746.
[6] 李永明，李平.模糊计算理论[M].北京：科学出版社，2016.LI Y M,LI P.Fuzzy computing theory[M].Beijing:Science Press,2016.
[7] 王熙照，谢凯.基于聚类的数据预处理对模糊决策树产生的影响[J].计算机工程与应用，2006,42(1):156-158,186.WANG X Z,XIE K.Clustering-based data preprocessing′s impact on fuzzy decision tree generation[J].Computer engineering and applications,2006,42(1):156-158,186.
[8] KHAZALI N,SHARIFI M,AHMADI M A.Application of fuzzy decision tree in EOR screening assessment[J].Journal of petroleum science and engineering,2019,177:167-180.
[9] CHEN Y L,HU H W,TANG K.Constructing a decision tree from data with hierarchical class labels[J].Expert systems with applications,2009,36(3):4838-4847.
[10] YUAN Y F,SHAW M J.Induction of fuzzy decision trees[J].Fuzzy sets and systems,1995,69(2):125-139.
[11] WANG X Z,YEUNG D S,TSANG E C C.A comparative study on heuristic algorithms for generating fuzzy decision trees[J].IEEE transactions on systems,man,and cybernetics,part B (cybernetics),2001,31(2):215-226.
[12] 王进，晏世凯，高延雨，等.基于MPI的ML-kNN算法并行[J].郑州大学学报(理学版),2018,50(3):34-38.WANG J,YAN S K,GAO Y Y,et al.Parallelization of ML-kNN Based on MPI[J].Journal of Zhengzhou university (natural science edition),2018,50(3):34-38.
[13] RABCAN J,RUSNAK P,KOSTOLNY J,et al.Comparison of algorithms for fuzzy decision tree induction[C]//2020 18th International Conference on Emerging eLearning Technologies and Applications.Slovenia:IEEE Press,2020:544-551.
[14] 杨蓓，缑西梅，艾艳.专家系统中的模糊知识表示及推理研究[J].郑州大学学报(理学版),2004,36(2):31-33.YANG B,GOU X M,AI Y.Study on the fuzzy knowledge representation and reasoning in expert system[J].Journal of Zhengzhou university (natural science edition),2004,36(2):31-33.
Basic Information:
DOI：10.13705/j.issn.1671-6841.2021199
China Classification Code:TP311.13
Citation Information:
[1]WANG Zhong,SHE Yanhong,ZHENG Yi.Fuzzy Decision Tree Construction Algorithm Based on Data with Hierarchical Labels[J].Journal of Zhengzhou University(Natural Science Edition),2022,54(02):24-31.DOI:10.13705/j.issn.1671-6841.2021199.
Fund Information:
国家自然科学基金项目(61976244); 陕西省自然科学基金项目(2021JQ-580)
2021-05-21
2021
2021-10-26
2021-12-16
2021
1
2021-11-09
2021-11-09
2021-11-09