A hybrid novel SWARA-ELECTRE-I method using probabilistic uncertain linguistic information for feature selection in image recognition

In the digital age, the exponential growth of data poses significant challenges for analysts and machine learning algorithms in pattern detection due to its high dimensionality. This study addresses the dimensionality problem by leveraging Probabilistic Uncertain Linguistic Term Set (PULTS), which combine Uncertain Linguistic Term Set (ULTS) with associated probabilities to handle uncertainty in decision-making. We introduce the PUL-weighted average operator to integrate the opinions of multiple decision-makers and propose a novel ELimination and Choice Translating REality (ELECTRE-I) method for optimizing alternatives in multiple attribute group decision-making (MAGDM) scenarios. This method is enhanced by the Stepwise Weight Assessment Ratio Analysis (SWARA) method to determine the relative weight of each attribute. By integrating SWARA with the ELECTRE-I method, we develop a comprehensive approach to tackle MAGDM problems using PULTS. A numerical example involving feature selection in image recognition demonstrates the method's effectiveness and accuracy. Comparative studies highlight the advantages of our approach in producing a small feature set with high classification accuracy. The proposed method offers a robust solution for feature selection in image recognition and other MAGDM problems, significantly improving decision-making accuracy and efficiency. The methodology's simplicity and computational ease make it applicable across various domains requiring effective dimensionality reduction.