MEBoost: Mixing estimators with boosting for imbalanced data classification

Rayhan, Farshid; Ahmed, Sajid; Mahbub, Asif; Jani, Md. Rafsan; Shatabda, Swakkhar; Farid, Dewan Md.; Rahman, Chowdhury Mofizur

doi:10.1109/skima.2017.8294128

Cited by 18 publications

(4 citation statements)

References 24 publications

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“…Furthermore, MEBoost has an early stopping criterion for the case that the improvement between two iterations of boosting is not significant. MEBoost only supports binary problems [55].…”

Section: Meboostmentioning

confidence: 99%

Boosting methods for multi-class imbalanced data classification: an experimental review

et al. 2020

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Imbalanced data set classification is a relatively new research line within the broader context of machine learning studies, which tries to learn from the skewed data distribution. A data set is imbalanced when the samples of one class consist of more instances than the rest of the classes in two-class and multi-class data sets [1]. Most of the standard machine learning algorithms show poor performance in this kind of datasets, because they tend to favor the majority class samples, resulting in poor predictive accuracy over the minority class [2]. Therefore, it becomes tough to learn the rare but

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“…Furthermore, MEBoost has an early stopping criterion for the case that the improvement between two iterations of boosting is not significant. MEBoost only supports binary problems [55].…”

Section: Meboostmentioning

confidence: 99%

Boosting methods for multi-class imbalanced data classification: an experimental review

et al. 2020

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“…The first fully connected layer in the network has 4096 neurons in the output and those were used to extract 4096 features from each dataset. For a fair sake of comparison, FRnet-2 was tested with several state of the art machine learning algorithms like, Decision Tree [40], SVM [25], MEBoost [38] and CUSBoost [37]. Each of these classifiers were fed the 4096 features generated by FRnet-1.…”

Section: Resultsmentioning

confidence: 99%

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

Rayhan

Ahmed

Mousavian

et al. 2020

Heliyon

Self Cite

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The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction. Two convolutional neural neworks are proposed where one model is used for feature manipulation and the other one for classification. Using the first method FRnet-1, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-2, to identify interaction probability employing those features. We have tested our method on four gold standard datasets exhaustively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three of the four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic(auROC) and area under Precision Recall curve(auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores.

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“…Farshid Rayhan et al [18] proposed MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost mixes two different weak learners with boosting to improve the performance on imbalanced datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

An under sampled k-means approach for handlingimbalanced data using diversified distribution

Shobana¹,

Battula²

2018

IJET

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Some true applications uncover troubles in taking in classifiers from imbalanced information. Albeit a few techniques for enhancing classifiers have been presented, the distinguishing proof of conditions for the effective utilization of the specific strategy is as yet an open research issue. It is likewise worth to think about the idea of imbalanced information, qualities of the minority class dissemination and their impact on arrangement execution. In any case, current investigations on imbalanced information trouble factors have been predominantly finished with manufactured datasets and their decisions are not effortlessly material to this present reality issues, likewise on the grounds that the techniques for their distinguishing proof are not adequately created. In this paper, we recommended a novel approach Under Sampling Utilizing Diversified Distribution (USDD) for explaining the issues of class lopsidedness in genuine datasets by thinking about the systems of recognizable pieces of proof and expulsion of marginal, uncommon and anomalies sub groups utilizing k-implies. USDD utilizes exceptional procedure for recognizable proof of these kinds of cases, which depends on breaking down a class dissemination in a nearby neighborhood of the considered case utilizing k-closest approach. The exploratory outcomes recommend that the proposed USDD approach performs superior to the looked at approach as far as AUC, accuracy, review and f-measure.

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MEBoost: Mixing estimators with boosting for imbalanced data classification

Cited by 18 publications

References 24 publications

Boosting methods for multi-class imbalanced data classification: an experimental review

Boosting methods for multi-class imbalanced data classification: an experimental review

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction

An under sampled k-means approach for handlingimbalanced data using diversified distribution

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