Using an attribute conversion approach for sample generation to learn small data with highly uncertain features

Li, Der-Chiang; Shi, Qi; Li, Ming Da

doi:10.1080/00207543.2018.1444813

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The core of these methods is enhancing the representativeness of the minority class. Synthetic minority oversampling Technique (SMOTE) [14] is one of the most popular oversampling methods. This technique uses a K-neighbors classification to define minority class samples as seed samples to produce more minority class samples, such that the dataset imbalance ratio can be reduced.…”

Section: Learning With Imbalanced Text Datamentioning

confidence: 99%

A Novel Classification Method Based on a Two-Phase Technique for Learning Imbalanced Text Data

Chen

Lin

et al. 2022

Symmetry

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The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.

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Section: Learning With Imbalanced Text Datamentioning

confidence: 99%