Genetic algorithms in classifier fusion

Gabrys, Bogdan; Ruta, Dymitr

doi:10.1016/j.asoc.2005.11.001

Cited by 84 publications

(43 citation statements)

References 7 publications

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“…The same basic concept has been given various names in the literature [63], amongst which the terms classifier fusion, classifier combination, multiple classifier system, and classifier ensemble are the most common. Even more are the different fusion operators that have been proposed in the literature, ranging from simple arithmetic operations to more complex combiners such as fuzzy integrals, decision templates, even genetic algorithms-based schemes, to only name a few [64][65][66][67][68].…”

Section: Decision Fusionmentioning

confidence: 99%

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral's spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

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Section: Decision Fusionmentioning

confidence: 99%

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

et al. 2014

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“…It has been observed by researchers that the performance of the ensemble classifiers is better than that of a single classifier. Some famous ensemble techniques are the mean, median, majority voting, and product-based techniques [20].…”

Section: Ensemble Classifiersmentioning

confidence: 99%

Optimized features selection for gender classification using optimization algorithms

Khan¹,

Nazir²,

Riaz³

2013

Turk J Elec Eng & Comp Sci

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Abstract:Optimized feature selection is an important task in gender classification. The optimized features not only reduce the dimensions, but also reduce the error rate. In this paper, we have proposed a technique for the extraction of facial features using both appearance-based and geometric-based feature extraction methods. The extracted features are then optimized using particle swarm optimization (PSO) and the bee algorithm. The geometric-based features are optimized by PSO with ensemble classifier optimization by the genetic algorithm. Using this approach, we have obtained promising results in terms of the classification error rate and computation time minimization. Moreover, our optimized feature-based method is robust to illumination, noise, and occlusion changes.

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“…The selected ensemble C * j is then combined to estimate the class labels ω k of the samples contained in the test dataset G. Figure 1 illustrates the OCS phases. Hence, the objective of OCS is to find the most relevant subset of classifiers based on the assumption that classifiers in C are redundant [6]. It is also interesting to note that the selection phase required by OCS can be easily formulated as an optimization problem in which a search algorithm operates by minimizing/maximizing one objective function or a set of objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the key challenge for classifier ensemble research is to understand and measure diversity in order to establish the perfect tradeoff between diversity and accuracy [6]. The literature has shown that OCS allows the selection of accurate and diverse classifier members [9] by using the combination of the error rate and diversity as search criteria.…”

Section: Introductionmentioning

confidence: 99%

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Pareto analysis for the selection of classifier ensembles

Santos

Sabourin

Maupin

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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The overproduce-and-choose strategy involves the generation of an initial large pool of candidate classifiers and it is intended to test different candidate ensembles in order to select the best performing solution. The ensemble's error rate, ensemble size and diversity measures are the most frequent search criteria employed to guide this selection. By applying the error rate, we may accomplish the main objective in Pattern Recognition and Machine Learning, which is to find high-performance predictors. In terms of ensemble size, the hope is to increase the recognition rate while minimizing the number of classifiers in order to meet both the performance and low ensemble size requirements. Finally, ensembles can be more accurate than individual classifiers only when classifier members present diversity among themselves. In this paper we apply two Pareto front spread quality measures to analyze the relationship between the three main search criteria used in the overproduce-and-choose strategy. Experimental results conducted demonstrate that the combination of ensemble size and diversity does not produce conflicting multi-objective optimization problems. Moreover, we cannot decrease the generalization error rate by combining this pair of search criteria. However, when the error rate is combined with diversity or the ensemble size, we found that these measures are conflicting objective functions and that the performances of the solutions are much higher.

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Genetic algorithms in classifier fusion

Cited by 84 publications

References 7 publications

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Optimized features selection for gender classification using optimization algorithms

Pareto analysis for the selection of classifier ensembles

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