Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

Krawczyk, Bartosz; Galar, Mikel; Jeleń, Łukasz; Herrera, Francisco

doi:10.1016/j.asoc.2015.08.060

Cited by 238 publications

(110 citation statements)

References 61 publications

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“…Multi-objective GP is also used to perform symbolic regression for imbalanced classification (Bhowan et al, 2013). Imbalanced classification is also addressed (Krawczyk et al, 2014) by learning cost-sensitive decision trees applying a GA or proposing evolutionary undersampling by the CHC application in order to obtain 1NN-based ensembles (Krawczyk et al, 2016).…”

Section: Classification (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

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Section: Classification (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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“…The entire evaluation includes mammography (usually the primary step), thermography [45,46], Doppler imagery [47,48] and elasticity analysis [49]. Such evaluation along with the computerized cytology [50,51] constitutes a basic architecture toward the ultimate goal of fully automatic clinical decision support systems for detection and grading of the breast cancer [52].…”

Section: Introductionmentioning

confidence: 99%

Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer

Kirimasthong

Rodtook

Chaumrattanakul

et al. 2016

Pattern Anal Applic

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Segmentation of ultrasound (US) images of breast cancer is one of the most challenging problems of modern medical image processing. A number of popular codes for US segmentation are based on the active contours (snakes) and on a variety of modifications of gradient vector flow. The snakes have been used to locate objects in various applications of medical images. However, the main difficulty in applying the method is initialization. Therefore, we suggest a new method for automatic initialization of active contours based on phase portrait analysis (PPA) of the underlying vector field and a sequential initialization of trial multiple snakes. The PPA makes it possible to exclude the noise and artifacts and properly initialize the multiple snakes. In turn, the trial snakes allow us to differentiate between the seeds initialized inside and outside the desired object. While preceding methods require the manual selection of at least one seed point inside the object or rely on the particular distribution of the gray levels, the proposed method is fully automatic and robust to the noise, as can be seen from the tests with synthetic and real images.

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“…Specifically, López et al [8] studied how six significant problems relating to the data intrinsic characteristics affected the performance of ensemble models for the IDL. Several superior ensemble models are based on boosting, such as EUSBoost [30], evolutionary undersampling boosting model in dealing with breast cancer malignancy classification [31]. Shi et al made use of bagging technique on SVM to cope with P300 detection problem [32].…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Wang

Luo

Huang

et al. 2017

Computational Intelligence and Neuroscience

117

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Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

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Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

Cited by 238 publications

References 61 publications

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

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