Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines

By Sebastián Maldonado, Richard Weber, Fazel Famili. Information Sciences, 2014

303 citations recorded by the source corpus.

Feature selection and classification of imbalanced data sets are two of the most interesting machine learning challenges, attracting a growing attention from both, industry and academia. Feature selection addresses the dimensionality reduction problem by determining a subset of available features to build a good model for classification or prediction, while the class-imbalance problem arises when the class distribution is too skewed. Both issues have been independently studied in the literature, and a plethora of methods to address high dimensionality as well as class-imbalance has been proposed. The aim of this work is to simultaneously explore both issues, proposing a family of methods that select those attributes that are relevant for the identification of the target class in binary classification. We propose a backward elimination approach based on successive holdout steps, whose contribution measure is based on a balanced loss function obtained on an independent subset. Our experiments are based on six highly imbalanced microarray data sets, comparing our methods with well-known feature selection techniques, and obtaining a better prediction with consistently fewer relevant features.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/00a47b1031b0fdb0c2b7035a51917feeb189aa26

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Citations in the corpus

Recent advances and emerging challenges of feature selection in the context of big data (2015, methodology): “Below we assess recent developments in solutions for high-dimensionality problems in areas such as clustering [33,34], regression [35–37] and classification [38,39].”
A review of feature selection methods with applications (2015, methodology): “The proposed approach was compared with distance-based and entropybased feature selection, and with SVM and neural network wrappers.”
Local structure preserving discriminative projections for RGB-D sensor-based scene classification (2015, background): “Subspace selection is necessary due to the dimensionality of the image feature representations [2,13,18,30,45] and has been extensively discussed in the literature.”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “This method is proposed in imbalanced data classification (Jamali et al. 2012; Maldonado et al. 2014; Van Hulse et al. 2009).”
Dual autoencoders features for imbalance classification problem (2016, methodology): “The SMOTE is also combined with a feature selection for training SVM to deal with imbalanced classification problems [15].”