A method for resampling imbalanced datasets in binary classification tasks for real-world problems

Cateni, Silvia; Colla, Valentina; Vannucci, Marco

doi:10.1016/j.neucom.2013.05.059

Cited by 148 publications

(63 citation statements)

References 17 publications

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“…Some hybrid methods are SMOTE-Tomek links (SMOTE-TL) [25], SMOTE-ENN [24], Borderline-SMOTE1 [26], Borderline-SMOTE2 [26], Safe-Level-SMOTE [27] and SMOTE-RSB [24]. In [28], a combination of over-sampling and under-sampling methods called SUNDO is proposed in which the loss of information is reduced.…”

Section: Data Preprocessing 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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Section: Data Preprocessing 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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“…The issue mentioned above is widely known and was addressed in several papers (e.g., Chawla et al, 2002;Liu et al, 2008;He and Garcia, 2009;Cateni et al, 2014;Beyan and Fisher, 2015). Generally, there are two popular methods dealing with class-imbalance problems: over-sampling the minority class and under-sampling the majority class.…”

Section: Comparison Of the Ovo And Ovr Methodsmentioning

confidence: 99%

Using the one–versus–rest strategy with samples balancing to improve pairwise coupling classification

Chmielnicki

Stąpor

2016

International Journal of Applied Mathematics and Computer Science

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The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against these classifiers and a voting scheme is applied using pairwise class probability estimates in a joint probability estimate for all classes. A closer look at the pairwise strategy shows the problem which impacts the final result. Each binary classifier votes for each object even if it does not belong to one of the two classes which it is trained on. This problem is addressed in our strategy. We propose to use additional classifiers to select the objects which will be considered by the pairwise classifiers. A similar solution was proposed by Moreira and Mayoraz (1998), but they use classifiers which are biased according to imbalance in the number of samples representing classes.

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“…The accuracy is calculated using the so called Balanced Classification Rate (BCR) [33] that considers the bal ance between the two class in order to be an appropriate measure for balanced and imbalanced data [34]. [35].…”

Section: )mentioning

confidence: 99%

Improving the stability of Sequential Forward variables selection

Cateni

Colla

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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Sequential Forward Selection is one of the most popular variable selection methods and is widely adopted in many applications, among which the design of classifiers. The main problem related to this approach regards the stability of the selection, namely the exploitation of different training data set can lead to the selection of different variable subsets. This problem is particularly critical in applications where variable selection is used to interpret the behaviour of the process or phenomenon under consideration, i.e. to understand which among a potentially huge list of variables actually affect the classification. The paper proposes a method that improves the stability of the sequential forward selection procedure while preserving and possibly improving the classification performance.

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A method for resampling imbalanced datasets in binary classification tasks for real-world problems

Cited by 148 publications

References 17 publications

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

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

Using the one–versus–rest strategy with samples balancing to improve pairwise coupling classification

Improving the stability of Sequential Forward variables selection

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