Comparative study of methods to obtain the number of hidden neurons of an auto-encoder in a high-dimensionality context

Vega-Gutierrez, Hector R.; Castorena, Carlos; Alejo, R.; Granda‐Gutiérrez, E. E.

doi:10.1109/tla.2020.9400448

Cited by 3 publications

(1 citation statement)

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“…[11]. Although several schemes for the automatic identification of the optimal number of neurons in a DNN exist [44], the test-and-trial method is widely used because of its suitability to be adapted to many scenarios. In addition, for the purpose of this study, we consider that the configuration of four to six hidden layers is appropriate to demonstrate the effectiveness of the methods to balance the dataset.…”

Section: Artificial Neural Network Architecturementioning

confidence: 99%

Density-Based Clustering to Deal with Highly Imbalanced Data in Multi-Class Problems

Munguía Mondragón,

Rendón Lara,

Alejo Eleuterio

et al. 2023

Mathematics

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In machine learning and data mining applications, an imbalanced distribution of classes in the training dataset can drastically affect the performance of learning models. The class imbalance problem is frequently observed during classification tasks in real-world scenarios when the available instances of one class are much fewer than the amount of data available in other classes. Machine learning algorithms that do not consider the class imbalance could introduce a strong bias towards the majority class, while the minority class is usually despised. Thus, sampling techniques have been extensively used in various studies to overcome class imbalances, mainly based on random undersampling and oversampling methods. However, there is still no final solution, especially in the domain of multi-class problems. A strategy that combines density-based clustering algorithms with random undersampling and oversampling techniques is studied in this work. To analyze the performance of the studied method, an experimental validation was achieved on a collection of hyperspectral remote sensing images, and a deep learning neural network was utilized as the classifier. This data bank contains six datasets with different imbalance ratios, from slight to severe. The experimental results outperform the classification measured by the geometric mean of the precision compared with other state-of-the-art methods, mainly for highly imbalanced datasets.

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Section: Artificial Neural Network Architecturementioning

confidence: 99%