From dynamic classifier selection to dynamic ensemble selection

Ko, Albert Hung-Ren; Sabourin, Robert; Britto, Alceu S.

doi:10.1016/j.patcog.2007.10.015

Cited by 368 publications

(237 citation statements)

References 21 publications

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“…For the calculation of the competence, various performance estimates are used, such as local accuracy estimation (Didaci et al, 2005), the Bayes confidence measure (Huenupán et al, 2008), multiple classifier behaviour (Giacinto and Roli, 2001), the oracle based measure (Ko et al, 2008), methods based on relating that of the classifier with the response obtained by random guessing (Woloszynski et al, 2012) or the randomized classification model (Woloszynski and Kurzynski, 2011), among others.…”

Section: Introductionmentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.

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Section: Introductionmentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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“…There are three main ways that classifiers can be selected in order to optimise the overall ensemble accuracy [5].…”

Section: A Ensemble Selection Strategiesmentioning

confidence: 99%

“…2) Local k-NN Ensemble Selection: Ko et al [5] suggest two methods for DES. KNORA-ELIMINATE considers the nearest K neighbours to x and uses classifiers that give a correct prediction for the K neighbouring points.…”

Section: ) Finding the Optimal Ensemblementioning

confidence: 99%

Dynamic ensemble selection methods for heterogeneous data mining

Ballard

Wang

2016

2016 12th World Congress on Intelligent Control and Automation (WCICA)

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Abstract-Big data is often collected from multiple sources with possibly different features, representations and granularity and hence is defined as heterogeneous data. Such multiple datasets need to be fused together in some ways for further analysis. Data fusion at feature level requires domain knowledge and can be time-consuming and ineffective, but it could be avoided if decision-level fusion is applied properly. Ensemble methods appear to be an appropriate paradigm to do just that as each subset of heterogeneous data sources can be separately used to induce models independently and their decisions are then aggregated by a decision fusion function in an ensemble. This study investigates how heterogeneous data can be used to generate more diverse classifiers to build more accurate ensembles. A Dynamic Ensemble Selection Optimisation (DESO) framework is proposed, using the local feature space of heterogeneous data to increase diversity among classifiers and Simulated Annealing for optimisation. An implementation example of DESO -BaggingDES is provided with Bagging as a base platform of DESO, to test its performance and also explore the relationship between diversity and accuracy. Experiments are carried out with some heterogeneous datasets derived from real-world benchmark datasets. The statistical analyses of the results show that BaggingDES performed significantly better than the baseline method -decision tree, and reasonably better than the classic Bagging.

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“…fausse si b = 0) simultanément. Différente stratégie ont été proposées pour sélectionner les classificateurs à inclure dans l'ensemble (Kim et Oh 2008, Ko et al 2008, Tsoumakas et al 2009, Yang 2011, Guo et Boukir 2013, Soto et al 2013. La stratégie utilisée ici est une évolution de l'algorithme « Selection by Accuracy and Diversity (SAD) » proposé par Yang (2011 Si tous les classificateurs ont été introduit dans l'ensemble comparer tous les ensembles classificateurs construit et sélectionner le meilleur, sinon, répéter les étapes 4 à 6.…”

Section: Ensemble Classificateursunclassified