Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets

Nguyen, Do Van; Ogawa, Kaoru; Matsumoto, Kazunori; Hashimoto, Masayuki

doi:10.1007/978-3-319-23868-5_9

Cited by 3 publications

(1 citation statement)

References 28 publications

(47 reference statements)

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“…In many approaches, fuzzy-rough set theory is applied before the classification methods, which needs considerable computation time. An approach is proposed in [35] that uses different criteria for the minority and majority classes in the fuzzy-rough instance selection and eliminates the time consuming step employed in the previous approaches.…”

Section: Related Workmentioning

confidence: 99%

Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification

Behmanesh,

Adibi,

Karshenas

2021

Preprint

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Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems. However, SVMs are likely to perform poorly in the classification of imbalanced data. The rough set theory presents a mathematical tool for inference in nondeterministic cases that provides methods for removing irrelevant information from data. In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data. The first innovation is introducing a new fuzzy rough set based under-sampling strategy to make the classifier robust in terms of the imbalanced data. For constructing the two proximal hyperplanes in FRLSTSVM, data points from the minority class remain unchanged while a subset of data points in the majority class are selected using a new method. In this model, we embed the weight biases in the LSTSVM formulations to overcome the bias phenomenon in the original twin SVM for the classification of imbalanced data. In order to determine these weights in this formulation, we introduce a new strategy that uses fuzzy rough set theory as the second innovation. Experimental results on the famous imbalanced datasets, compared to the related traditional SVM-based methods, demonstrate the superiority of the proposed

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Section: Related Workmentioning

confidence: 99%