Cost-sensitive Feature Selection for Support Vector Machines

Benítez-Peña, Sandra; Blanquero, Rafael; Carrizosa, Emilio; Ramírez‐Cobo, Pepa

doi:10.1016/j.cor.2018.03.005

Cited by 43 publications

(26 citation statements)

References 37 publications

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“…The feature selection problem has been widely studied in the multivariate data literature. See for instance the surveys [27], the papers [2] addressing classification problems, [33] for regression, and [28] for clustering. Particularly, some references in very high-dimensional problems such as cancer detection via gene expression data, must be mentioned.…”

Section: Introductionmentioning

confidence: 99%

Variable selection in classification for multivariate functional data

Blanquero

Carrizosa

Jiménez-Cordero

et al. 2019

Information Sciences

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When classification methods are applied to high-dimensional data, selecting a subset of the predictors may lead to an improvement in the predictive ability of the estimated model, in addition to reducing the model complexity. In Functional Data Analysis (FDA), i.e., when data are functions, selecting a subset of predictors corresponds to selecting a subset of individual time instants in the time interval in which the functional data are measured. In this paper, we address the problem of selecting the most informative time instants in multivariate functional data, a case much less studied than its single-variate counterpart. Our proposal allows one to use in a very simple way high-order information of the data, e.g. monotonicity or convexity by means of the functional data derivatives. The aforementioned problem is addressed with tools of Global Optimization in continuous variables: the time instants are selected to maximize the correlation between the class label and the Support Vector Machine score used for classification. The effectiveness of the proposal is shown in univariate and multivariate datasets.

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Section: Introductionmentioning

confidence: 99%

Variable selection in classification for multivariate functional data

Blanquero

Carrizosa

Jiménez-Cordero

et al. 2019

Information Sciences

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“…Another approach to account for multiple objectives is to consider a single-objective optimization problem with a budget constraint. In some studies, researchers suggest minimizing the number of features given that a certain level of performance is achieved [3,29], whereas others optimize predictive performance under the budget constraint for the cost of included features [26]. Both these directions require setting a specific threshold to introduce a budget constraint, either for the model performance or for the number of used features.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In the US, the total outstanding consumer credit amount exceeded $3,831 billion 2 . At the same time, the delinquency rate on consumer loans by commercial banks experienced a growth of more than 11% since 2015 3 . The rise of default rates emphasizes the importance of accurately deciding upon loan provisioning, which is a task of credit scoring.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective approach for profit-driven feature selection in credit scoring

Kozodoi

Lessmann

Παπακωνσταντίνου³

et al. 2019

Decision Support Systems

111

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In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a multi-objective wrapper framework based on the NSGA-II genetic algorithm with two fitness functions: the Expected Maximum Profit (EMP) and the number of features. Experiments on multiple credit scoring data sets demonstrate that the proposed approach develops scorecards that can yield a higher expected profit using fewer features than conventional feature selection strategies.

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“…Support Vector Machine (SVM) is a machine learning algorithm based on the statistical theory [17]. SVM cannot only overcome the defects of traditional prediction methods, but also has strong generalization ability and robustness [18], [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

Xia

Zhu

et al. 2020

IEEE Access

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The frequent bankruptcy incidents of peer-to-peer (P2P) lending industry have damaged the benefits of investors in China, and how to accurately and efficiently predict the investment risks of P2P lending becomes an important problem. For this very reason, a novel prediction method based on improved binary glowworm swarm optimization and multi-fractal dimension (IBGSOMFD) for P2P lending investment risk is proposed. Firstly, we propose an improved binary glowworm swarm optimization, abbreviated IBGSO, by uniformly designing an initial population using the good-point set theory, improving the moving way of glowworms, and introducing the mechanism of population diffusion and variation. Secondly, IBGSO combined with multi-fractal dimension (MFD) is applied to feature selection, and the optimal subset extracted from the original dataset can be efficiently achieved utilizing IBGSO, which can reduce its redundant attributes, and retain its pivotal attributes of P2P lending investment risk. Finally, an investment risk prediction model of P2P lending based on support vector machine (SVM) is established, which can accurately and efficiently predict the investment risk of P2P lending. Experimental results on 6 University of California Irvine (UCI) benchmark datasets show that IBGSOMFD outperforms other stateof-the-art approaches in predictive ability and calculative efficiency, and its effectiveness and significance. After the performance verification of IBGSOMFD, this work looks at how it can be applied in the risk prediction of P2P lending investment in China to maintain a stable market order. INDEX TERMS Improved binary glowworm swarm optimization, multi-fractal dimension, P2P lending investment risk, prediction.

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Cost-sensitive Feature Selection for Support Vector Machines

Cited by 43 publications

References 37 publications

Variable selection in classification for multivariate functional data

Variable selection in classification for multivariate functional data

A multi-objective approach for profit-driven feature selection in credit scoring

A Novel Prediction Method Based on Improved Binary Glowworm Swarm Optimization and Multi-Fractal Dimension for P2P Lending Investment Risk

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