Applying fuzzy analytic hierarchy process to select models developed by abstraction and decision fusion architecture (case study: classification of Persian handwritten characters)

This research presents an application-oriented approach for developing machine learning models that consider the trade-off between model accuracy, processing speed, and efficient resource utilization, focusing on applications such as wearable smart systems.

A set of models is developed based on the Abstraction and Decision Fusion Architecture (ADFA), and then, using a multi-criteria decision-making approach, the appropriate models for the intended application are identified. The proposed methodology has three main phases: 1) developing models based on the ADFA, 2) defining evaluation criteria, and 3) selecting models using the Fuzzy Analytic Hierarchy Process (FAHP).

The experimental results of this research demonstrate the effectiveness of this approach in developing suitable machine learning models for applications related to wearable devices, such as smart glasses.

Originality/Value: 

This research introduces three innovations: 1) the use of ADFA for developing models for the classification of Persian handwritten characters, 2) defining a new abstraction for summarizing handwritten character images, and 3) developing a fuzzy multi-criteria decision-making approach for mapping the developed models in the ADFA to real-world applications.