Classifier combination has been investigated as a new research field to improve recognition reliability by taking into account the complementarity between classifiers, in particular for automatic semantic-based video content indexing and retrieval. Many combination schemes have been proposed in the literature according to the type of information provided by each classifier as well as their trai...

A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental re...

Combination of classifiers is a promising direction for obtaining better classification systems. However, the outputs of different classifiers may have different scales and hence the classifier outputs are incomparable. Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to avoid this problem, the measurement level ...

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...

Classifier combination is a technique that often provides appreciable accuracy gains. In this paper, we argue that the underlying statistical model of classifier combination should be made explicit. Using directed graphical models (DGMs), we provide representations of two common combination schemes, the mean and product rules. We also introduce new DGMs that yield novel combination rules. We fi...

Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Näıve Bayes, Cosine, Decision List, Transformation-based Learning and...

The aim of this paper is to investigate the role of the a-priori knowledge in the process of classifier combination. For this purpose three combination methods are compared which use different levels of a-priori knowledge. The performance of the methods are measured under different working conditions by simulating sets of classifier with different characteristics. For this purpose, a random var...

Automatic signal type identification (ASTI) is an important topic for both the civilian and military domains. Most of the proposed identifiers can only recognize a few types of digital signal and usually need high levels of SNRs. This paper presents a new high efficient technique that includes a variety of digital signal types. In this technique, a combination of higher order moments and hi...