Majority vote decision fusion system to assist automated identification of vertebral column pathologies

Authors : Akın ÖZÇİFT, Mehmet BOZUYLA

Pages : 53-65

View : 16 | Download : 13

Publication Date : 2023-03-28

Article Type : Research Paper

Abstract :This paper presents a majority vote decision fusion system called AIVCP insert ignore into journalissuearticles values(Automated Identification of Vertebral Column Pathologies);. With this aim, we proposed a three-step decision fusion algorithm: In the first step, a pool of algorithms from different groups is obtained and the number of classifiers is decreased to 10 with the use of prediction accuracy and classifier diversity concept. As a second step, different majority vote combinations of 10 algorithms are searched with a grid search strategy guided on top of 10-fold cross validation evaluation and with prediction error analysis. In the second step, we obtained four base classifiers, i.e., Naïve Bayes insert ignore into journalissuearticles values(NB);, Simple Logistics insert ignore into journalissuearticles values(SL);, Learning Vector Quantization insert ignore into journalissuearticles values(LVQ); and Decision Stump insert ignore into journalissuearticles values(DS); whose majority vote decision fusion generate the most accurate diagnosis rate in Vertebral Column Pathologies domain. As the third step, we applied a Support Vector Machine based feature selection to increase prediction performance of the proposed system further. The experiments are evaluated with the use of 10-fold cross-validation, Sensitivity, Specificity and Confusion Matrices. The experimental results have shown that NB, SL, LVQ, and DS as single classifiers generate 82.58%, 87.09%, 82.90%, and 77.41% average diagnosis accuracies respectively. On the other hand, majority vote decision fusion of these single predictors produces 90.32% accuracy that is higher than each of the constituents. The resultant diagnosis accuracy of Vote algorithm for Vertebral column pathologies is quite promising.
Keywords : Majority voting, decision fusion, multiple classifier systems