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The paper is organized as follows: Section 2 provides the preliminaries, which include the fundamental definitions of the M-CFIS model, CFTL, and tree. Section 3 investigates the novel CFRG-based complex fuzzy transfer learning. Section 4 evaluates the proposed RTrieCFTL by running it on both real-life and benchmark data sets. To conclude, the last part must outline this research’s future work. | Journal of Computer Science and Cybernetics V.40 N.1 2024 23 36 DOI no. 10.15625 1813-9663 19160 THE NOVEL CFRG -BASED COMPLEX FUZZY TRANSFER LEARNING SYSTEM TRIEU THU HUONG1 2 3 LUONG THI HONG LAN4 1 Graduate University of Science and Technology Vietnam Academy of Science and Technology Ha Noi Viet Nam 2 Faculty of Management Information Systems Banking Academy Ha Noi Viet Nam 3 Artificial Intelligence Research Center VNU Information Technology Institute Vietnam National University Ha Noi Viet Nam 4 Faculty of Computer Science and Engineering Thuyloi University Ha Noi Viet Nam 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 are important 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 .
