Diversity techniques improve the performance of the best imbalance learning ensembles

Díez-Pastor, José F.; Rodrı́guez, Juan J.; García‐Osorio, César; Kuncheva, Ludmila I.

doi:10.1016/j.ins.2015.07.025

Cited by 174 publications

(101 citation statements)

References 39 publications

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“…3 -5. There are some differences in positioning of different algorithms by various measures. This fact was also pointed out by T. Raeder et al in [55], J. J. Rodriguez et al in [56] and J. F. Dez-Pastor et al in [12]. In our tests the highest average ranking position has the RandomForest (RF) classifier regardless of adopted measure.…”

Section: Experiments #2supporting

confidence: 85%

The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

Bach

Werner

Żywiec³

et al. 2017

Information Sciences

115

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Section: Experiments #2supporting

confidence: 85%

The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

Bach

Werner

Żywiec³

et al. 2017

Information Sciences

115

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“…S1). However, in SMOTE algorithm, the same number of the synthetic data are generated for each minority sample without consideration to neighboring samples, which may increase the overlapping between classes273132. More details about SMOTE algorithm can be found in ref.…”

Section: Theory and Methodsmentioning

confidence: 99%

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

Tharwat

Moemen

Hassanien

2016

Sci Rep

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Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

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“…The current work is strongly motivated by previously published approaches 29,49 for ensemble construction. Diversity and quality are two important issues for the performance of ensemble models.…”

Section: Adaptive Ensemble Classication Framework (Aecf)mentioning

confidence: 99%

“…However, the benets of these preprocess techniques may vary in characteristics of datasets. 20,29 Furthermore, some potential useful data may be omitted when modeling on a very small balanced subset from the original data by some methods such as the undersampling based approaches. 30 The algorithm level methods reduce the sensitiveness to class unbalance by modications of existing classication algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A novel adaptive ensemble classification framework for ADME prediction

Yang

Shi

et al. 2018

RSC Adv.

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It has now become clear that in silico prediction of ADME (absorption, distribution, metabolism, and elimination) characteristics is an important component of the drug discovery process. Therefore, there has been considerable interest in the development of in silico modeling of ADME prediction in recent years. Despite the advances in this field, there remains challenges when facing the unbalanced and high dimensionality problems simultaneously. In this work, we introduce a novel adaptive ensemble classification framework named as AECF to deal with the above issues. AECF includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble. We considered five sampling methods, seven base modeling techniques, and ten ensemble rules to build a choice pool. The proper route of constructing predictive models was determined automatically according to the imbalance ratio (IR). With the adaptive characteristics of AECF, it can be used to work on the different kinds of ADME data, and the balanced data is a special case in AECF. We evaluated the performance of our approach using five extensive ADME datasets concerning Caco-2 cell permeability (CacoP), human intestinal absorption (HIA), oral bioavailability (OB), and Pglycoprotein (P-gp) binders (substrates/inhibitors, PS/PI). The performance of AECF was evaluated on two independent datasets, and the average AUC values were 0.8574-0.8602, 0.8968-0.9182, 0.7821-0.7981, 0.8139-0.8311, and 0.8874-0.8898 for CacoP, HIA, OB, PS and PI, respectively. Our results show that AECF can provide better performance and generality compared with individual models and two representative ensemble methods bagging and boosting. Furthermore, the degree of complementarity among the AECF ensemble members was investigated for the purpose of elucidating the potential advantages of our framework. We found that AECF can effectively select complementary members to construct predictive models by our auto-adaptive optimization approach, and the additional diversity in both sample and feature space mainly contribute to the complementarity of ensemble members.

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Diversity techniques improve the performance of the best imbalance learning ensembles

Cited by 174 publications

References 39 publications

The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

A novel adaptive ensemble classification framework for ADME prediction

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