Title: A novel classification tree based on local minimum Gini index and attribute partial order structure diagram

Authors: Cunfang Zheng

Addresses: School of Electrical Engineering, Liren College, Yanshan University, Qinhuangdao 066004, Hebei Province, China

Abstract: Decision tree is not only an important machine learning method, but also the basis of ensemble learning methods such as random forest and deep forest. Based on the theory of Formal Concept Analysis (FCA) and Attribute Partial Order Structure Diagram (APOSD), a new decision tree for classification is proposed in this paper. Firstly, the local minimum of Gini index is used to complete the data granulation, and the Formal Decision Mode Information Table (FDMIT) is constructed. Then, the Attribute Partial Order Classification Tree (APOCT) is generated based on APOSD to complete the pattern recognition and rule extraction. The method of APOCT separates the process of granulation and visualisation, and the granulation process is easy to parallelise and efficient. The experimental results show that APOCT is effective.

Keywords: classification tree; decision tree; partial order; Gini index; data granulation; formal concept analysis; knowledge discovery.

DOI: 10.1504/IJCAT.2020.111083

International Journal of Computer Applications in Technology, 2020 Vol.64 No.1, pp.33 - 45

Received: 01 Apr 2020
Accepted: 25 May 2020
Published online: 09 Nov 2020 *

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