THE NOVEL TREE-BASED COMPLEX FUZZY TRANSFER LEARNING SYSTEM

Trieu Thu Huong, Luong Thi Hong Lan
Author affiliations

Authors

  • Trieu Thu Huong Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Ha Noi, Viet Nam
  • Luong Thi Hong Lan Faculty of Computer Science and Engineering, Thuyloi University, Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/1813-9663/19160

Keywords:

Complex fuzzy set, Mamdani complex fuzzy inference system, Transfer learning, Fuzzy transfer learning, Complex fuzzy transfer learning, Complex fuzzy rule tree

Abstract

Today, the rapid development of the internet has led to a data explosion; the complex fuzzy transfer learning (CFTL) model has received increasing attention from the academic community due to its various real-world applications, such as solar activity, digital signal processing, time series forecasting, etc. CFTL combines Transfer learning and Complex Fuzzy Logic in a framework to solve the problem of learning tasks with no prior direct contextual knowledge, which is stored, retrieved, and organized in the data structure. Data structures play an important role in computational intelligence because they are key performance indicators for systems or models. Therefore, to improve the performance of the previous CFTL, this paper investigates a novel complex fuzzy decision tree (CFDT) structure to represent the complex fuzzy rules and provides a transfer learning model for a complex fuzzy inference system. In contrast with prior axis-parallel decision trees in which only a single feature or variable is considered at each node, the node of the proposed decision tree structures includes complex fuzzy inference rules that contain multiple elements. Multiple features for each node help minimize the size. To prove the efficiency of the proposed framework, we carry out extension experiments on numerous instances (datasets). Experimental results demonstrate/exhibit that our offered performs better than the prior framework regarding accuracy and the size of the produced trees.

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Published

20-03-2024

How to Cite

[1]
T. T. Huong and L. T. H. Lan, “THE NOVEL TREE-BASED COMPLEX FUZZY TRANSFER LEARNING SYSTEM”, JCC, vol. 40, no. 1, Mar. 2024.

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