The objective of this paper is to show that a combination of votes from various pattern classifiers is better than a single vote from each individual classifier. A proposed support function is used in the combination of votes. The combination of outputs is motivated by the fact that decisions made by teams are generally better than those made by individuals. The decision maker at the outputs of...

The use of artificial outputs generated by a classifier simulator has recently emerged as a new trend to provide an underlying evaluation of classifier combination methods. In this paper, we propose a new method for the artificial generation of classifier outputs based on additional parameters which provide sufficient diversity to simulate, for a problem of any number of classes and any type of...

Multi-classifier combination based on Dempster-Shafer theory of evidence has demonstrated it’s superior performance. In the approach based on Dempster-Shafer theory, the basic probability assignments for evidence are usually derived from classifiers’ global performance. However, our study discovered that while using classifiers’ global performance as basic probability assignments doesn’t necess...

This work presents the application of weighted majority voting technique for combination of classification decision obtained from three Multi_Layer Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari characters using three different feature sets. The features used are intersection, shadow feature and chain code histogram features. Shadow features are computed globally for...

Wheat industry is an important constituent of Northern China’s overall agricultural economy. Proper disease detection using computer vision and pattern recognition has being investigated to minimize the loss, and finally achieve intelligent healthy farming. This paper proposes a new strategy of Multi-Classifier System based on SVM (support vector machine) for pattern recognition of wheat leaf d...

The combination of classifiers has been proposed as a method allowing to improve the quality and the hardiness of recognition systems as compared to a single classifier. This paper describes a new segmentation scheme based on a combination of pixel classifications. The aim of this paper is to show the influence of the neighborhood information and of the number of classifiers used in the combina...

Rapid popularization of smart cell phone equipped with camera has led to a number of new legal and criminal problems related to multimedia such as digital image, which makes cell phone source identification an important branch of digital image forensics. This paper proposes a classifier combination based source identification strategy for cell phone images. To identify the outlier cell phone mo...

Classifier combination through ensemble systems is one of the most effective approaches to improve accuracy classification systems. Ensemble are generally used combine classifiers; However, selecting best individual classifiers a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and genetic algorithm for selection classifiers. Our called ...