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Cited by 4 publications
(5 citation statements)
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“…This finding shows that DP performs better than methods proposing triads including by default the best performing base classifier. 41 Furthermore, DP showed a monotonic increase across the voting performance spectrum (R>0.95) regardless of the testing dataset suggesting that it closely matches the voting performance. This matching was achieved even for datasets where voting EM performed lower than the best base classifier (i.e.…”
Section: Discussionmentioning
confidence: 81%
See 1 more Smart Citation
“…This finding shows that DP performs better than methods proposing triads including by default the best performing base classifier. 41 Furthermore, DP showed a monotonic increase across the voting performance spectrum (R>0.95) regardless of the testing dataset suggesting that it closely matches the voting performance. This matching was achieved even for datasets where voting EM performed lower than the best base classifier (i.e.…”
Section: Discussionmentioning
confidence: 81%
“…[36][37][38][39] Previous studies tried to incorporate the classifiers' diversity in order to construct successful ensembles by using different methods such as clustering, pruning, proper weighting based on various diversity measures, fuzzy logic, greedy search, particle swarm optimization, random sampling or data manipulation. 19,20,23,26,[40][41][42][43] Another important criterion is the performance of individual base classifiers to be combined during voting. Although it would seem intuitive to combine the best performing classifiers during voting, previous studies have shown that the best classification is not always achieved by combining classifiers that show the best individual performance.…”
Section: Introductionmentioning
confidence: 99%
“…The dimension of the lowdimensional space is denoted as p . A typical value for this parameter, also used here, is 2 p  [53][54][55].…”
Section: Clustering Of Dtw Distancesmentioning
confidence: 99%
“…This procedure enables the use of standard techniques, such as cluster analysis, to discover the underlying distribution of the original data. The dimension of the low-dimensional space is denoted as p. A typical value for this parameter, also used here, is p = 2 [53][54][55].…”
Section: Clustering Of Dtw Distancesmentioning
confidence: 99%
“…Work [3] has novel application of this technique in reinforcement systems in-volving numerous agents. It is also being applied in image processing field by researchers in works like [4] Novel application in voting system is done by work [5].…”
Section: Literature Surveymentioning
confidence: 99%