scholarly journals Classifier Ensemble Recommendation

2012 ◽  
pp. 209-218 ◽  
Author(s):  
Pyry Matikainen ◽  
Rahul Sukthankar ◽  
Martial Hebert
Keyword(s):  
Classifier Ensemble

Discrimination of Breast Tumors in Ultrasonic Images by Classifier Ensemble Trained with AdaBoost

2009 ◽  
Vol 129 (4) ◽  
pp. 620-629
Author(s):  
Atsushi Takemura ◽  
Akinobu Shimizu ◽  
Kazuhiko Hamamoto
Keyword(s):  
Breast Tumors ◽  
Classifier Ensemble ◽  
Ultrasonic Images

scholarly journals Dental Implants Success Prediction by Classifier Ensemble on Imbalanced Data

2021 ◽  
pp. 100021
Author(s):  
Mostafa Sabzekar ◽  
Motahare Namakin ◽  
Hanie Alipoor Shahr Babaki ◽  
Arash Deldari ◽  
Vahide Babaiyan
Keyword(s):  
Dental Implants ◽  
Imbalanced Data ◽  
Classifier Ensemble ◽  
Success Prediction

Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

2010 ◽  
Vol 50 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Michael C. Lee ◽  
Lilla Boroczky ◽  
Kivilcim Sungur-Stasik ◽  
Aaron D. Cann ◽  
Alain C. Borczuk ◽  
...  
Keyword(s):  
Feature Selection ◽  
Pulmonary Nodules ◽  
Computer Aided Diagnosis ◽  
Classifier Ensemble ◽  
Computer Aided ◽  
Aided Diagnosis

A Multicriteria Weighted Vote-Based Classifier Ensemble for Heart Disease Prediction

2015 ◽  
Vol 32 (4) ◽  
pp. 615-645 ◽  
Author(s):  
Saba Bashir ◽  
Usman Qamar ◽  
Farhan Hassan Khan
Keyword(s):  
Heart Disease ◽  
Classifier Ensemble ◽  
Disease Prediction ◽  
Weighted Vote

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

scholarly journals GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System

Sensors ◽  
2008 ◽  
Vol 8 (10) ◽  
pp. 6203-6224 ◽  
Author(s):  
Rongwu Xu ◽  
Lin He
Keyword(s):  
Genetic Algorithm ◽  
Classifier Ensemble ◽  
Sensor System
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