Journal ArticleDOI
Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data
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TLDR
A new cost-sensitive algorithm (CSMLP) is presented to improve the discrimination ability of (two-class) MLPs and it is theoretically demonstrated that the incorporation of prior information via the cost parameter may lead to balanced decision boundaries in the feature space.Abstract:
Traditional learning algorithms applied to complex and highly imbalanced training sets may not give satisfactory results when distinguishing between examples of the classes. The tendency is to yield classification models that are biased towards the overrepresented (majority) class. This paper investigates this class imbalance problem in the context of multilayer perceptron (MLP) neural networks. The consequences of the equal cost (loss) assumption on imbalanced data are formally discussed from a statistical learning theory point of view. A new cost-sensitive algorithm (CSMLP) is presented to improve the discrimination ability of (two-class) MLPs. The CSMLP formulation is based on a joint objective function that uses a single cost parameter to distinguish the importance of class errors. The learning rule extends the Levenberg-Marquadt's rule, ensuring the computational efficiency of the algorithm. In addition, it is theoretically demonstrated that the incorporation of prior information via the cost parameter may lead to balanced decision boundaries in the feature space. Based on the statistical analysis of results on real data, our approach shows a significant improvement of the area under the receiver operating characteristic curve and G-mean measures of regular MLPs.read more
Citations
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Learning from class-imbalanced data
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A Survey of Predictive Modeling on Imbalanced Domains
TL;DR: The main challenges raised by imbalanced domains are discussed, a definition of the problem is proposed, the main approaches to these tasks are described, and a taxonomy of the methods are proposed.
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Imbalanced Deep Learning by Minority Class Incremental Rectification
TL;DR: In this paper, a batchwise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training is proposed to minimize the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes.
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Deep Imbalanced Learning for Face Recognition and Attribute Prediction
TL;DR: Cluster-based Large Margin Local Embedding (CLMLE), when combined with a simple k-nearest cluster algorithm, shows significant improvements in accuracy over existing methods on both face recognition and face attribute prediction tasks that exhibit imbalanced class distribution.
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Deep Learning in Microscopy Image Analysis: A Survey
TL;DR: A snapshot of the fast-growing deep learning field for microscopy image analysis, which explains the architectures and the principles of convolutional neural networks, fully Convolutional networks, recurrent neural Networks, stacked autoencoders, and deep belief networks and their formulations or modelings for specific tasks on various microscopy images.
References
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SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.