Research Area: Machine Learning
The accumulated historical data are beneficial for generating solutions that are more satisfactory to decision makers because their preferences and experience are characterized by historical data. However, this might be infeasible when only few data are available. Suppose that the few data are collected from a domain called the target domain. There may be some domains correlated to the target domain, which are called source domains. The data from source domains might be useful for helping generate solutions to the problem in the target domain. Following this idea, this paper proposes a cross-domain decision making method based on the combination of TrAdaBoost, an instance-based transfer learning method, and a decision making method in the context of the evidential reasoning approach. This may be the first attempt to combine transfer learning with a decision making method to help generate high-quality solutions satisfactory to decision makers when only few data are available for the problem in the target domain. A data selection strategy is designed to increase the similarity between the data from source and target domains and a weight initialization strategy is designed based on the available gold standards. The two strategies are intended for improving the performance of the proposed method. With the two strategies, the process of the proposed method is presented. The effectiveness of the proposed method is validated by its application in helping diagnose breast lesions with the diagnostic data of five radiologists collected from a tertiary hospital located in Hefei, Anhui, China.
Keywords:
Cross-domain decision making
Evidential reasoning approach
Instance-based transfer learning
TrAdaBoost
Diagnosis of breast lesions
Machine Learning
Deep Learning
Author(s) Name: Chao Fu, Zijian Wu, Min Xue & Weiyong Liu
Journal name: Artificial Intelligence Review
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s10462-022-10267-5
Volume Information:
