conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...

Multiple sensor fusion and binary decision tree classifiers have been used to successfully solve many real world problems. These topics are usually studied separately. Fusion of binary decision tree classifiers in a multiple sensor environment has received very little attention. In this paper, we formulate the problem, investigate its scope, outline some issues associated with decision tree cla...

This notebook paper describes the four runs submitted by IRIT at TRECVid 2010 Semantic Indexing task. The four submitted runs can be described and compared as follows: • Run 4 – late fusion (weighted sum) of multiple audio-only classifiers output • Run 3 – context-aware re-rank of run 4 using hidden Markov model • Run 2 – context-aware late fusion of multiple audio classifiers output with hidde...

Multiple classifiers combination is a technique that combines the decisions of different classifiers as to reduce the variance of estimation errors and improve the overall classification accuracy. A new multiple classifiers fusion method integrated classifier selection and classifier combination is proposed in this paper. It is base on interval-valued fuzzy permutation. Firstly, normalize all c...

This paper presents multiple functional neural fuzzy networks (FNFN) fusion using fuzzy integral (FI). Since the classifiers are able to complement each other, the fusion of multiple classifiers overcomes the limitations of applying a single classifier. In addition, the FI is a better decision-combination scheme than the majority voting method that uses the subjectively defined relevance of cla...

The paper presents a multimodal approach for biometric authentication, based on multiple classifiers. The proposed solution uses a post-classification biometric fusion method in which the biometric data classifiers outputs are combined in order to improve the overall biometric system performance by decreasing the classification error rates. The paper shows also the biometric recognition task im...

Fusion is a popular practice to combine multiple classifiers or multiple modalities in biometrics. In this paper, optimal decision fusion (ODF) by AND rule and OR rule is presented. We show that the decision fusion can be done in an optimal way such that it always gives an improvement in terms of error rates over the classifiers that are fused. Both the optimal decision fusion theory and the ex...

State-of-the-art language identification systems are commonly constructed with multiple parallel classifiers to take advantage of different levels of speech features. These classifiers are combined with a fusion module to make the final decision. Following the maximum a posteriori (MAP) decision rule, the fusion of multiple classifiers can be transformed to a constrained optimal linear combinat...

This paper presents a novel method for combining multiple compensatory neural fuzzy networks (CNFN) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complement each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A comb...

Classification is a recurrent task of determining a target function that maps each attribute set to one of the predefined class labels. Ensemble fusion is one of the suitable classifier model fusion techniques which combine the multiple classifiers to perform high classification accuracy than individual classifiers. The main objective of this paper is to combine base classifiers using ensemble ...