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

This paper presents a majority vote decision fusion system called AIVCP (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 (NB), Simple Logistics (SL), Learning Vector Quantization (LVQ) and Decision Stump (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,

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