Methods for Designing Multiple Classifier Systems

Roli, Fabio; Giacinto, Giorgio; Vernazza, G.

doi:10.1007/3-540-48219-9_8

Cited by 131 publications

(78 citation statements)

References 8 publications

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“…Moreover test and select methods implicitly include a "production stage", by which a set of classifiers must be generated. Different selection methods based on different search algorithm mututated from feature selection methods (forward and backward search) or for the solution of complex optimization tasks (tabu search) are proposed in [109]. Another interesting approach uses clustering methods and a misure of diversity to generate sets of diverse classifiers combined by majority voting, selecting the ensemble with the highest performance [48].…”

Section: Mixtures Of Experts Methodsmentioning

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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Section: Mixtures Of Experts Methodsmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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“…The design of a successful ensemble consists of two important parts [25,26]: (1) the design of the individual classifiers (Section 3.1); (2) the design of the aggregation mechanism (Section 3.2) [27].…”

Section: The Ensemblementioning

confidence: 99%

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Maio

Rossetti

Zio

2017

Science and Technology of Nuclear Installations

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Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

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“…Voting denotes the simplest method of combining multiple classifiers [15]. In its simplest form, called plurality or majority voting, each classifier contributes with a single vote [16].…”

Section: Majority Voting Classifiermentioning

confidence: 99%

A majority voting classifier with probabilistic guarantees

Manganini

Falsone

Prandini

2015

2015 IEEE Conference on Control Applications (CCA)

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Abstract-This paper deals with supervised learning for classification. A new general purpose classifier is proposed that builds upon the Guaranteed Error Machine (GEM). Standard GEM can be tuned to guarantee a desired (small) misclassification probability and this is achieved by letting the classifier return an unknown label. In the proposed classifier, the size of the unknown classification region is reduced by introducing a majority voting mechanism over multiple GEMs. At the same time, the possibility of tuning the misclassification probability is retained. The effectiveness of the proposed majority voting classifier is shown on both synthetic and real benchmark datasets, and the results are compared with other well-established classification algorithms.

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Methods for Designing Multiple Classifier Systems

Cited by 131 publications

References 8 publications

Ensembles of Learning Machines

Ensembles of Learning Machines

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

A majority voting classifier with probabilistic guarantees

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