scholarly journals Risk prediction of product-harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China

2016 ◽  
Vol 33 (3) ◽  
pp. 254-274 ◽  
Author(s):  
Delu Wang ◽  
Jianping Zheng ◽  
Gang Ma ◽  
Xuefeng Song ◽  
Yun Liu
Keyword(s):  
Experimental Study ◽  
Risk Prediction ◽  
Rough Sets ◽  
Listed Companies ◽  
Classifier Fusion ◽  
Multiple Classifier

scholarly journals Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion

2011 ◽  
Vol 10 (1) ◽  
pp. 3 ◽  
Author(s):  
Zeinab Mahmoudi ◽  
Saeed Rahati ◽  
Mohammad Ghasemi ◽  
Vahid Asadpour ◽  
Hamid Tayarani ◽  
...  
Keyword(s):  
Cochlear Implantation ◽  
Hearing Aid ◽  
Classifier Fusion ◽  
Voice Disorder ◽  
Multiple Classifier

Speaker verification with multiple classifier fusion using Bayes based confidence measure

2007 ◽  
Author(s):  
Fernando Huenupán ◽  
Nestor Becerra Yoma ◽  
Carlos Molina ◽  
Claudio Garreton
Keyword(s):  
Speaker Verification ◽  
Classifier Fusion ◽  
Confidence Measure ◽  
Multiple Classifier

Intelligent Classifiers Fusion for Enhancing Recognition of Genes and Protein Pattern of Hereditary Diseases

2011 ◽  
pp. 220-248
Author(s):  
Parthasarathy Subhasini ◽  
Bernadetta Kwintiana Ane ◽  
Dieter Roller ◽  
Marimuthu Krishnaveni
Keyword(s):  
Intelligent Systems ◽  
Protein Pattern ◽  
Hereditary Diseases ◽  
Classifier Fusion ◽  
Levels Of Abstraction ◽  
Multiple Classifier ◽  
Individual Classifier ◽  
Intelligent Models ◽  
Multiple Experts ◽  
Level Fusion

Most the objective of intelligent systems is to create a model, which given a minimum amount of input data or information, is able to produce reliable recognition rates and correct decisions. In the application, when an individual classifier has reached its limit and, at the same time, it is hard to develop a better one, the solution might only be to combine the existing well performing classifiers. Combination of multiple classifier decisions is a powerful method for increasing classification rates in difficult pattern recognition problems. To achieve better recognition rates, it has been found that in many applications, it is better to fuse multiple relatively simple classifiers than to build a single sophisticated classifier. Such classifiers fusion seems to be worth applying in terms of uncertainty reduction. Different individual classifiers performing on different data would produce different errors. Assuming that all individual methods perform well, intelligent combination of multiple experts would reduce overall classification error and as consequence increase correct outputs. To date, content interpretation still remains as a highly complex task which requires many features to be fused. However, the fusion mechanism can be done at different levels of the classification. The fusion process can be carried out on three levels of abstraction closely connected with the flow of the classification process, i.e. data level fusion, feature level fusion, and classifier fusion. The work presented in this chapter focuses on the fusion of classifier outputs for intelligent models.

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