Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

Cruz, Rafael M. O.; Souza, Mariana Assunção de; Sabourin, Robert; Cavalcanti, George D. C.

doi:10.1142/s0218001419400093

Cited by 15 publications

(7 citation statements)

References 49 publications

(48 reference statements)

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“…With the extensive application of ensemble approaches, it has become an important issue for designing a more efficient ensemble classification algorithm. Compared with static ensemble algorithms, dynamic selection ensemble algorithms [ 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ] have been shown to effectively improve the F-measure and G-mean values. A dynamic selection ensemble algorithm predicts the label of the test sample by evaluating the capability level of each classifier and selects the set of the most capable or competitive classifiers.…”

Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…In the second scenario, combining the optimised pool of base classifiers and the optimised resampling methods was considered. The parameter settings for the resampling and dynamic selection methods are adopted from the studies in [11,24]. Meanwhile for the heterogeneous ensemble, the scheme was composed of three different optimised pools of classifiers, namely k-NN, random forest and decision tree reported in experiment 3. performance.…”

Section: Methodsmentioning

confidence: 99%

“…The techniques are selected based on their reported improved performances from studies in [9][10][11]23]. The six strategies are briefly described as obtained from [9,24]. Figure 2 depicts the three steps in the DS approach.…”

Section: Dynamic Selectionmentioning

confidence: 99%

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Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection

et al. 2021

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Automatic anomaly detection monitoring plays a vital role in water utilities’ distribution systems to reduce the risk posed by unclean water to consumers. One of the major problems with anomaly detection is imbalanced datasets. Dynamic selection techniques combined with ensemble models have proven to be effective for imbalanced datasets classification tasks. In this paper, water quality anomaly detection is formulated as a classification problem in the presences of class imbalance. To tackle this problem, considering the asymmetry dataset distribution between the majority and minority classes, the performance of sixteen previously proposed single and static ensemble classification methods embedded with resampling strategies are first optimised and compared. After that, six dynamic selection techniques, namely, Modified Class Rank (Rank), Local Class Accuracy (LCA), Overall-Local Accuracy (OLA), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U) and Meta-Learning for Dynamic Ensemble Selection (META-DES) in combination with homogeneous and heterogeneous ensemble models and three SMOTE-based resampling algorithms (SMOTE, SMOTE+ENN and SMOTE+Tomek Links), and one missing data method (missForest) are proposed and evaluated. A binary real-world drinking-water quality anomaly detection dataset is utilised to evaluate the models. The experimental results obtained reveal all the models benefitting from the combined optimisation of both the classifiers and resampling methods. Considering the three performance measures (balanced accuracy, F-score and G-mean), the result also shows that the dynamic classifier selection (DCS) techniques, in particular, the missForest+SMOTE+RANK and missForest+SMOTE+OLA models based on homogeneous ensemble-bagging with decision tree as the base classifier, exhibited better performances in terms of balanced accuracy and G-mean, while the Bg+mF+SMENN+LCA model based on homogeneous ensemble-bagging with random forest has a better overall F1-measure in comparison to the other models.

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“…In this paper, we propose an enhanced DES method for imbalance classification and apply it to China corporate bond default prediction. The proposed technology is inspired by technique of data sampling [25], metalearning framework [26], [27], technique of diversity [28] and weighted fusion strategy [29]. In particular, it directly address the imbalanced data sets and can comprehensively consider multiple criteria for selecting competent and diverse classifiers.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

Wang

Zhang

Yan³

2023

IEEE Access

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China corporation bond default prediction is important and can be formulated as an imbalance classification problem solved by static ensemble classifiers. However, dynamic ensemble selection (DES) classifiers have not been applied to this typical problem in the context of business research. DES classifiers are capable of selecting an ensemble classifier for each test sample, leading to better classification performance than static ensemble classifiers. Most existing DES classifiers can not address imbalance classification optimally and only use single criteria of competent for classifier selection, resulting in sub-optimal classification performance. In this paper, we propose an enhanced DES classifier, named META-DES-Diversity, that inherits strengths of data sampling, meta-learning, criteria of diversity, and dynamic weighting fusion scheme to alleviate such limitations. Specifically, the synthetic minority over-sampling technique is initially used to balance the training set before generating a candidate classifier pool. To select an ensemble classifier with a highly competent, the meta-learning framework META-DES is utilized to consider multiple criteria of competence. In complement with the meta-leaning framework, a two-phase selection strategy is utilized to perform competence and diverse ensemble classifier selection. Note that a competencedriven decision fusion scheme is employed to effectively fuse classification results from selected ensemble classifiers. Experiments on 14 two-class imbalanced data sets from the KEEL repository and one self-collected China corporation bond data set show the effectiveness and superiority of the proposed enhanced DES classifier.

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Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

Cited by 15 publications

References 49 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

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