Combining the results of several neural network classifiers

Rogova, Galina L.

doi:10.1016/0893-6080(94)90099-x

Cited by 324 publications

(79 citation statements)

References 3 publications

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“…Various authors have investigated the use of Dempster-Shafer theory for combining the results of different classifiers [2,6,10,15]. However, the aim of using Dempster-Shafer theory in this context is quite different from our aim in this paper.…”

Section: Related Workcontrasting

confidence: 45%

Combining Multi-resolution Evidence for Georeferencing Flickr Images

Laere

Schockaert

Dhoedt

2010

Lecture Notes in Computer Science

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Abstract. We explore the task of determining the geographic location of photos on Flickr, using combined evidence from Naive Bayes classifiers that are trained at different spatial resolutions. In particular, we estimate the location of Flickr photos, based on their tags, at four different scales, ranging from a city-level granularity to fine-grained intra-city areas. Using Dempster-Shafer's evidence theory, we combine the output of the different classifiers into a single mass assignment. We demonstrate experimentally that the induced belief and plausibility measures are useful to determine whether there is sufficient evidence to classify the photo at a given granularity. Thus an adaptive method is obtained, by which photos are georeferenced at the most appropriate resolution.

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Section: Related Workcontrasting

confidence: 45%

Combining Multi-resolution Evidence for Georeferencing Flickr Images

Laere

Schockaert

Dhoedt

2010

Lecture Notes in Computer Science

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“…The effectiveness of an ensemble can be measured by the extent to which the members are error-independent (show different patterns of generalization) [19]. The ideal would be a set of models where each of the models generalize well, and when they do make errors on new data, these errors are not shared with any other models [19].…”

Section: Ensemble Modelingmentioning

confidence: 99%

“…The ideal would be a set of models where each of the models generalize well, and when they do make errors on new data, these errors are not shared with any other models [19].…”

Section: Ensemble Modelingmentioning

confidence: 99%

Solving Regression Problems Using Competitive Ensemble Models

Frayman

Rolfe

Webb

2002

Lecture Notes in Computer Science

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Abstract. The use of ensemble models in many problem domains has increased significantly in the last few years. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results show a substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.

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“…Where we interpret the classifier outputs as the support for the classes, fuzzy aggregation methods can be applied, such as simple connectives between fuzzy sets or the fuzzy integral [23,22,66,128]; if the classifier outputs are possibilistic, Dempster-Schafer combination rules can be applied [108]. Statistical methods and similarity measures to estimate classifier correlation have also been used to evaluate expert system combination for a proper design of multi-expert systems [58].…”

Section: Non-generative Ensemblesmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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Abstract. Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

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Combining the results of several neural network classifiers

Cited by 324 publications

References 3 publications

Combining Multi-resolution Evidence for Georeferencing Flickr Images

Combining Multi-resolution Evidence for Georeferencing Flickr Images

Solving Regression Problems Using Competitive Ensemble Models

Ensembles of Learning Machines

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