Design of effective neural network ensembles for image classification purposes

By Giorgio Giacinto, Fabio Roli. Image and Vision Computing, 2001

458 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Media Technology

View Paper: https://www.semanticscholar.org/paper/9b61fc9392201fe81941b8e523ac66494179bc93

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Citations in the corpus

Layered Ensemble Architecture for Time Series Forecasting (2016, background): “accurate and diverse networks are then combined to obtain the ensemble output [46], [47].”
Ensemble approaches for regression: A survey (2012, methodology): “The algorithms tested are: FSS selecting the best model in the first iteration and selecting randomly a model for the first iteration, BSS, tabu search, Giacinto & Roli’s clustering algorithm [66], and some others.”
A survey of multiple classifier systems as hybrid systems (2014, background): “Pairwise measures averaging a measure between each classifier pair in an ensemble, such as Q-statistic [58], kappa-statistics [63], disagreement [64] and double-fault measure [61,65].”
Methods for Designing Multiple Classifier Systems (2001, methodology): “Accordingly, some researchers proposed the so-called “overproduce and choose” paradigm (also called “test and select” approach [6]) in order to design the MCS most appropriate for the task at hand [7, 8, 9].”
Classifier selection for majority voting (2005, background): “Keywords: Classifier fusion; Classifier selection; Diversity; Search algorithms; Majority voting; Generalisation”
An analysis of diversity measures (2006, methodology): “These procedures can be illustrated using two examples, a boosting algorithm and the neural network ensemble proposed by Giacinto and Roli (2001) .”
Multiple Classifier Systems (2000, methodology): “Recently, an experimental study has been conducted to evaluate the results of simple majority vote and Dempster-Shafer combination method in relation to the degree of correlation among the experts [15].”
A relative evaluation of multiclass image classification by support vector machines (2004, unknown): “accurate image classifications [38]–[41].”
Relationships between combination methods and measures of diversity in combining classifiers (2002, unknown): “3.1.4 The double-fault measure (DF) (used in [ 27 ])”