FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cruz, Rafael M. O.; Oliveira, Dayvid V. R.; Cavalcanti, George D. C.; Sabourin, Robert

doi:10.1016/j.patcog.2018.07.037

Cited by 50 publications

(22 citation statements)

References 39 publications

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“…Moreover, to address the problem of borderline samples in the local region, the Frienemy Indecision REgion-DES (FIRE-DES) method introduces a notion of "frienemy" by pre-selecting classifiers that correctly classify at least one pair of samples from different classes [53]. FIRE-DES++ is an extension of FIRE-DES to ameliorate the noise sensitivity and indecision region problems [54]. It removes the noises and reduces the overlap of classes in the validation set and then applies K-Nearest Neighbors Equality (KNNE) to define a more balanced RoC.…”

Section: Region Of Competencementioning

confidence: 99%

“…and ℎ are META-DES parameters that were set to 5 and 1, respectively, and ∆ is a DDES-I parameter that was set to 0.1 × . See similar settings in [48], [53], [54].…”

Section: Table 3 Base Classifiers and Hyperparameter Poolsmentioning

confidence: 99%

“…• Following guidelines in the literature [53], [54], [77], the Wilcoxon signed rank test with an adjusted alpha risk of 0.0014 (≈ 0.05/36) was used to determine whether the differences in classification performance of competing methods were statistically significant. Table 4 presents the comparative classification accuracies of the seven benchmark methods in conjunction with those of the proposed methods 1 .…”

Section: Table 3 Base Classifiers and Hyperparameter Poolsmentioning

confidence: 99%

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DDES: A Distribution-Based Dynamic Ensemble Selection Framework

Choi

Lim

2021

IEEE Access

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Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases. In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case. Traditional DES methods rely on the Euclidean distancebased k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble. However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes. This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points. The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods. Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.

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Section: Region Of Competencementioning

confidence: 99%

“…and ℎ are META-DES parameters that were set to 5 and 1, respectively, and ∆ is a DDES-I parameter that was set to 0.1 × . See similar settings in [48], [53], [54].…”

Section: Table 3 Base Classifiers and Hyperparameter Poolsmentioning

confidence: 99%

Section: Table 3 Base Classifiers and Hyperparameter Poolsmentioning

confidence: 99%

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DDES: A Distribution-Based Dynamic Ensemble Selection Framework

Choi

Lim

2021

IEEE Access

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“…In contrast, in dynamic ensemble classifiers (DES), the decision of which classifiers should be combined for generating the final output is postponed until generalization phase [ 39 – 45 ]. In other words, there will be not a fixed subset of classifiers which applies to any test instance.…”

Section: Related Workmentioning

confidence: 99%

HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets

Ostvar

Moghadam

2020

Computational Intelligence and Neuroscience

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In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.

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“…Thus, defining an area of competence with unbiased validation data instead of biased data. The results show significant improvement when compared to the existing dynamic pruning methods [46] Bernard et al [2009] proposed a different approach to add the trees to random forest based on feature selection strategy. The aim was to point out that random forest subsets performs better than original random forest.…”

Section: Random Forest-specific Pruning Techniquesmentioning

confidence: 99%

A Survey of Random Forest Pruning Techniques

Rose¹,

Hassen²

2019

9th International Conference on Computer Science, Engineering and Applications (ICCSEA 2019)

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Random Forest is an ensemble machine learning method developed by Leo Breiman in 2001. Since then, it has been considered the state-of-the-art solution in machine learning applications. Compared to the other ensemble methods, random forests exhibit superior predictive performance. However, empirical and statistical studies prove that the random forest algorithm generates unnecessarily large number of base decision trees. This may cost high computational efficiency, predictive time, and occasional decrease in effectiveness. In this paper, Authors survey existing random forest pruning techniques and compare the performance between them. The research revolves around both the static and dynamic pruning technique and analyses the scope of improving the performance of random forest by techniques including generating diverse and accurate decision trees, selecting high performance subset of decision trees, genetic algorithms and other state of art methods, among others.

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FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cited by 50 publications

References 39 publications

DDES: A Distribution-Based Dynamic Ensemble Selection Framework

DDES: A Distribution-Based Dynamic Ensemble Selection Framework

HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets

A Survey of Random Forest Pruning Techniques

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