The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

At present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of “dynamic classifier selection” as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theore...

Classifier combination falls in the so called machine learning area. Its aim is to combine some classification paradigms in order to improve the individual accuracy of the component classifiers. Classifier hierarchies are an alternative among the several methods of classifier combination. In this paper we present new results about a recently proposed hierarchy construction method. Experiments h...

In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting class...

We propose a new technique for normalizing likelihood of multiple classifiers prior to their combination. Our technique takes classifier-specific likelihood characteristics into account and maps them to a common, ideal characteristic allowing fair combination under arbitrary combination schemes. For each classifier, a simple warping process aligns the likelihood with the accumulated recognition...

When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a Fuzzy Random Forest, is proposed. This ...

In this thesis, a multi-source system for human identification is developed. The system uses three sources: face classifier, height classifier, and color classifier. In the process of developing this system, classifier combination and the integration of classifer outputs over sequences of data points were studied in detail. The method of classifier combination used relies on weighing classifier...

BACKGROUND User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. METHODS We conducted experiments on online user reviews using di...

A Meta Classifier in this approach is used for the approval of Loan application, as a Data mining classification tool to support Business operations in a very secure way. The goal of designing a Meta classifier system is to achieve the best possible classification performance for the task of effective decision making. This Meta classifier is the combination of Naïve Bayesian classifier, K-Neare...