FRbE
Fuzzy Rank-based Ensemble
The motive for ensembling is to utilize each of the confidence factors generated from base learners fully by mapping them into non-linear functions. One of the mapped values signifies the abidance or closeness to 1 and the other one signifies the deviation from 1. This proposed approach overcomes the shortcoming of the conventional ranking methods. The scores from base learners are mapped on two different functions having different concavities to generate non-linear fuzzy ranks and generate a fused score by combining these two ranks, which helps us to quantify the total deviation from expected. Lesser the deviation shows better confidence towards a particular class. The class having the lowest deviation value is considered as the winner and is assigned as the final class value. Here, we first give a brief overview of the pre-trained CNN models used as base learners.
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical Cytology
https://paperswithcode.com/paper/a-fuzzy-rank-based-ensemble-of-cnn-models-for
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical Cytology