A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

Yin, Qingyan; Zhang, Jiangshe; Ji, Nannan

doi:10.1155/2014/358942

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…This characteristic is common in data arising in diverse domains: fraud detection, intrusion detection, medical diagnosis and monitoring, bioinformatics, text categorisation, image processing are only few examples. Traditional classification algorithms are biased to the overrepresented class and produce an unacceptably low classification rate for the minority class [ 5 ]. Over the last decade, ensemble-based classification systems have remained the centre of attention, gaining popularity, and demonstrating their applicability for class imbalanced data [ 6 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

et al. 2016

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Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

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Section: Introductionmentioning

confidence: 99%

“…To deal with imbalanced datasets, several homogeneous EoCs have been proposed [ 5 , 15 – 18 ]. In addition, oversampling, undersampling, and sampling by synthetically generating some instances are commonly used sampling techniques for improving the classification performance [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

et al. 2016

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“…These methods are based on employing multiple classifiers and incorporating their results. Ensemble learning methods are usually combined with the previous mentioned approaches to be efficiently used [40][41][42]. Bagging and boosting methods are within this group that use preprocessing at their first step, so they may suffer from over-fitting and under-fitting [43].…”

Section: Ensemble Learning Methodsmentioning

confidence: 99%

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Nikpour

Nezamabadi‐pour

2018

Iran J Comput Sci

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Imbalanced data sets are those in which data samples have uneven distribution amongst the classes. When classifying such data, classical classifiers encounter problem; hence, this problem has become a challenging issue in the field of machine learning. To weaken this problem, we propose a novel hyper-heuristic algorithm, called HTSS, to select the best training samples in this paper. In other words, the best training sample subset is chosen with the goal of enhancing the performance of classifier when confronting imbalanced data. To do so, some local search algorithms and a choice function are incorporated with a global search algorithm to improve its effectiveness. The global search used in this paper is binary quantum inspired gravitational search algorithm (BQIGSA) which is a recently proposed meta-heuristic search for optimization of binary encoded problems. Experiments are performed on 75 imbalanced data sets, and G-mean and AUC measures are employed for evaluation. The results of comparing the proposed method with other state of the art algorithms show the superiority of the proposed HTSS method.

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“…We faced imbalanced problem and small amount of features when dealing with this data. To improve the performances of the methods, EasyEnsemble 30 , 31 and feature engineering were adopted. The whole algorithms was shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Different protein-protein interface patterns predicted by different machine learning methods

Wang

Yang

Yin

et al. 2017

Sci Rep

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Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

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A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

Cited by 12 publications

References 29 publications

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Different protein-protein interface patterns predicted by different machine learning methods

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