Classifiers Fusion with Data Dependent Aggregation Schemes
نویسنده
چکیده
In this paper we studied two different classifiers fusion algorithms exploiting the combination weights expressed over the entire data space and the combination with data dependent weights. The following aggregation schemes are employed in the study: the majority vote, the averaging, the combination via Choquet integral with the − λ fuzzy measure, the combination via space partitioning and classifier selection approach, and the combination via Choquet integral with the data dependent − λ fuzzy measure.
منابع مشابه
Feature Based Architecture for Decision Fusion
Researchers continue to focus on the design of pattern recognition systems to achieve the best classification rates. Usually, different classification schemes are developed for the problem at hand, and then by experimental assessment, the best classifier is chosen as the final solution to the problem. However, it has been observed that although one design may outperform the others, the patterns...
متن کاملThe NTU Toolkit and Framework for High-Level Feature Detection at TRECVID 2007
In TRECVID 2007 high-level feature (HLF) detection, we extend the well-known LIBSVM and develop a toolkit specifically for HLF detection. The package shortens the learning time and provides a framework for researchers to easily conduct experiments. We efficiently and effectively aggregate detectors of training past data to achieve better performances. We propose post-processing techniques, conc...
متن کاملSteganalysis with Classifier Combinations
Blind steganalysis is based on choice of the feature set and the machine learning classifiers used for classification. While the performance of individual classifiers is good, the classification accuracy is seen to increase by appropriate combination of classifiers. This research has implemented image steganalysis with fusion of classifiers by various data fusion schemes. We intend to analyse t...
متن کاملOn the use of Heronian means in a similarity classifier
This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here ar...
متن کاملCombination of Feature Selection and Learning Methods for IoT Data Fusion
In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set ba...
متن کامل