Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data

Yijing, Li; Guo, Hao; Liu, Xiao; Li, Yanan; Li, Jinling

doi:10.1016/j.knosys.2015.11.013

Cited by 116 publications

(67 citation statements)

References 34 publications

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“…Another notable work is that of [23] which uses the receiver operating characteristics (ROC) to imply that the significant features could be obtained using a technique called "feature assessment by sliding thresholds" (FAST)", but the ROC is a "what-if" conditional probability simulations scenario, and in reality, such a condition may not arise. The work of [24] uses adaptations of the ensemble (combinations) of multiple classifiers based on feature selection, re-sampling, and algorithm learning. In line with using ensemble approaches to feature selections, a method called MIEE (mutual information-based feature selection for EasyEnsemble) was proposed by [25].…”

Section: The Feature Selection Approachmentioning

confidence: 99%

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Ebenuwa¹,

Sharif²,

Al-Nemrat³

et al. 2019

Symmetry

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Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets.

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Section: The Feature Selection Approachmentioning

confidence: 99%

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Ebenuwa¹,

Sharif²,

Al-Nemrat³

et al. 2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…Prominent and recent examples include the SMOTEBoost [44], SMOTEBagging [45], RB-Bagging [46], NBBag [47], and EUSBoost [48] methods. Very recent proposals also deal with multi-class imbalanced data [49].…”

Section: Imbalanced Classificationmentioning

confidence: 99%

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

et al. 2016

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Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of realworld situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.

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“…RF demonstrated its efficiency in solving different image classification problems [14,25], which reflects the power of the ensemble classifiers technique. In addition to significantly improving the segmentation results when compared with single classifiers, ensemble classifiers based methods are powerful in address-ing several known classification problems such as imbalanced correlation and over-fitting [21]. However, such combination technique is not enough to fully exploit the training of classifiers and leverage their strengths.…”

Section: Introductionmentioning

confidence: 99%

Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation

2018

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We introduce a dynamic multiscale tree (DMT) architecture that learns how to leverage the strengths of different state-of-the-art classifiers for supervised multi-label image segmentation. Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier nodes to gradually improve their performances. Our DMT is a generic classification model that inherently embeds different cascades of classifiers while enhancing learning transfer between them to boost up their classification accuracies. Specifically, each node in our DMT can nest a Structured Random Forest (SRF) classifier or a Bayesian Network (BN) classifier. The proposed SRF-BN DMT architecture has several appealing properties. First, while SRF operates at a patch-level (regular image region), BN operates at the super-pixel level (irregular image region), thereby enabling the DMT to integrate multi-level image knowledge in the learning process. Second, although BN is powerful in modeling dependencies between image elements (superpixels, edges) and their features, the learning of its structure and parameters is challenging. On the other hand, SRF may fail to accurately detect very irregular object boundaries. The proposed DMT robustly overcomes these limitations for both classifiers through the ascending and descending flow of contextual information between each parent node and its children nodes. Third, we train DMT using different scales for input patches and superpixels. Basically, as we go deeper along the tree edges nearing its leaf nodes, we progressively decrease the patch and superpixel sizes, producing segmentation maps that capture a coarse-to-fine image details. Last, DMT demonstrates its outperformance in ⇤ Corresponding author: irekik@dundee.ac.uk, www.basira-lab. com comparison to several state-of-the-art segmentation methods for multi-labeling of brain images with gliomas.

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Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data

Cited by 116 publications

References 34 publications

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation

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