Probabilistic Predictions of Ensemble of Classifiers Combined with Dynamically Weighted Majority Vote

2011 ◽  
Author(s):  
Eftim Zdravevski ◽  
Andrea Kulakov ◽  
Slobodan Kalajdziski ◽  
Danco Davcev
Keyword(s):  
Majority Vote ◽  
Ensemble Of Classifiers ◽  
Weighted Majority ◽  
Weighted Majority Vote

scholarly journals A Weighted Majority Vote Strategy Using Bayesian Networks

2013 ◽  
pp. 219-228 ◽  
Author(s):  
Luigi P. Cordella ◽  
Claudio De Stefano ◽  
Francesco Fontanella ◽  
Alessandra Scotto di Freca
Keyword(s):  
Bayesian Networks ◽  
Majority Vote ◽  
Vote Strategy ◽  
Weighted Majority ◽  
Weighted Majority Vote

scholarly journals Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume

2008 ◽  
Author(s):  
Akinobu Shimizu ◽  
Takuya NARIHIRA ◽  
Daisuke FURUKAWA ◽  
Hidefumi KOBATAKE ◽  
Shigeru NAWANO ◽  
...  
Keyword(s):  
Majority Vote ◽  
Liver Lesion ◽  
Segmentation Algorithm ◽  
Tumor Segmentation ◽  
Abdominal Ct ◽  
Adaboost Algorithm ◽  
Contrast Enhanced ◽  
Weak Hypothesis ◽  
Weighted Majority Vote ◽  
Ct Volume

This paper describes an ensemble segmentation trained by the AdaBoost algorithm, which finds a sequence of weak hypotheses, each of which is appropriate for the distribution on training example, and combines the weak hypotheses by a weighted majority vote. In our study, a weak hypothesis corresponds to a weak segmentation process. This paper shows a procedure for generating an ensemble segmentation algorithm using AdaBoost, and applies it to a liver lesion extraction problem from a contrast enhanced abdominal CT volume. A leave-one-patient-out validation test using 16 CT volumes demonstrated the effectiveness of the generated ensemble segmentation algorithm. In addition, we evaluated the performance by applying the algorithm to unknown test data provided by the �3D Liver Tumor Segmentation Challenge 2008�.

CLASSIFIER COMBINATION BY BAYESIAN NETWORKS FOR HANDWRITING RECOGNITION

2009 ◽  
Vol 23 (05) ◽  
pp. 887-905 ◽  
Author(s):  
CLAUDIO DE STEFANO ◽  
CIRO D'ELIA ◽  
ALESSANDRA SCOTTO DI FRECA ◽  
ANGELO MARCELLI
Keyword(s):  
Neural Networks ◽  
Bayesian Network ◽  
Majority Vote ◽  
Handwriting Recognition ◽  
Back Propagation ◽  
Borda Count ◽  
Classifier Combination ◽  
Classifier Systems ◽  
Weighted Majority Vote ◽  
Definition Of

In the field of handwriting recognition, classifier combination received much more interest than the study of powerful individual classifiers. This is mainly due to the enormous variability among the patterns to be classified, that typically requires the definition of complex high dimensional feature spaces: as the overall complexity increases, the risk of inconsistency in the decision of the classifier increases as well. In this framework, we propose a new combining method based on the use of a Bayesian Network. In particular, we suggest to reformulate the classifier combination problem as a pattern recognition one in which each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is then used to automatically infer the probability distribution for each class and eventually to perform the final classification. Experiments have been performed by using two different pools of classifiers, namely an ensemble of Learning Vector Quantization neural networks and an ensemble of Back Propagation neural networks, and handwritten specimen from the UCI Machine Learning Repository. The obtained performance has been compared with those exhibited by multi-classifier systems adopting the classifiers, but three of the most effective and widely used combining rules: the Majority Vote, the Weighted Majority Vote and the Borda Count.

scholarly journals Group decisions based on confidence weighted majority voting

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sascha Meyen ◽  
Dorothee M. B. Sigg ◽  
Ulrike von Luxburg ◽  
Volker H. Franz
Keyword(s):  
Cognitive Modeling ◽  
Group Discussion ◽  
Group Processes ◽  
Majority Voting ◽  
Group Decisions ◽  
Optimal Method ◽  
Weighted Majority ◽  
Real Group ◽  
Lower Accuracy ◽  
Weighted Majority Voting

Abstract Background It has repeatedly been reported that, when making decisions under uncertainty, groups outperform individuals. Real groups are often replaced by simulated groups: Instead of performing an actual group discussion, individual responses are aggregated by a numerical computation. While studies have typically used unweighted majority voting (MV) for this aggregation, the theoretically optimal method is confidence weighted majority voting (CWMV)—if independent and accurate confidence ratings from the individual group members are available. To determine which simulations (MV vs. CWMV) reflect real group processes better, we applied formal cognitive modeling and compared simulated group responses to real group responses. Results Simulated group decisions based on CWMV matched the accuracy of real group decisions, while simulated group decisions based on MV showed lower accuracy. CWMV predicted the confidence that groups put into their group decisions well. However, real groups treated individual votes to some extent more equally weighted than suggested by CWMV. Additionally, real groups tend to put lower confidence into their decisions compared to CWMV simulations. Conclusion Our results highlight the importance of taking individual confidences into account when simulating group decisions: We found that real groups can aggregate individual confidences in a way that matches statistical aggregations given by CWMV to some extent. This implies that research using simulated group decisions should use CWMV instead of MV as a benchmark to compare real groups to.

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