In this paper we study the effectiveness of using multiple classifier combination for EEG signals classification aiming to obtain more accurate results than it possible from single classifier system. The developed system employs different features vectors fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority vo...

In this paper we compare a number of classifier fusion approaches within a complete and efficient framework for video shot indexing and retrieval. The aim of the fusion stage of our sytem is to detect the semantic content of video shots based on classifiers output obtained from low level features. An overview of current research in classifier fusion is provided along with a comparative study of...

This paper describes a crowdsourcing system that integrates machine learning techniques with human classifiers, showing how to apply a Bayesian approach to classifier combination to the challenge of crowdsourcing document topic labels. First, we use a number of NLP techniques to extract informative document features. We then screen and select workers using Amazon Mechanical Turk to label select...

This paper presents a perturbation-based approach useful to select the best combination method for a multi-classifier system. The basic idea is to simulate small variations in the performance of the set of classifiers and to evaluate to what extent they influence the performance of the combined classifier. In the experimental phase, the Behavioural Knowledge Space and the Dempster-Shafer combin...

Multiple classifier systems based on neural networks can give improved generalisation performance as compared with single classifier systems. We examine collaboration in multi-net systems through in-situ learning, exploring how generalisation can be improved through the simultaneous learning in networks and their combination. We present two in-situ trained systems; first, one based upon the sim...

This paper presents a new classifier combination technique based on the DempsterShafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to t...

In this work, we use the output of a symbolic prominence classifier rather than acoustic cues of prominence, to improve the tasks of clustering and classification of spontaneous conversations to topics. In our experiments, we combine the output of a prominence classifier with lexical feature selection and combination methods to build improved feature subsets. Evaluated for the task of topic cla...

This paper evaluates NN and HMM classifiers applied to the handwritten word recognition problem. The goal is analyse the individual and combined performance of these classifiers. They are evaluated considering two different combination strategies and the experiments are performed with the same database and similar feature sets. The strategy proposed takes advantage of the different but compleme...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...

Image classification is one of the most important tasks of remote sensing information processing used for object recognition. In this paper, a novel scheme is proposed to improve the accuracy of hyperspectral image classification by amalgamating multiple feature vector sets and ensemble methods with different classifiers. Extracting the texture, color and object features of the satellite images...