Data Augmentation by Guided Deep Interpolation

Szlobodnyik, Gergely; Farkas, Lóránt

doi:10.1016/j.asoc.2021.107680

Cited by 14 publications

(2 citation statements)

References 30 publications

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“…In the paper, we have considered a linear combination technique for the oversampling of the data; we may apply many other techniques [66,67] for the same purpose and make a comparative study among them, which is also the future scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Naushin

Das

Nayak

et al. 2023

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Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal with the problem, they have yet to consider how to select a proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory to the imbalanced data learning problem could be a promising research direction that generates the synthetic data and removes the outliers. The proposed work identifies the positive, boundary, and negative regions of the target set using the rough set theory and removes the objects in the negative region as outliers. It also explores the positive and boundary regions of the rough set by applying the fuzzy theory to generate the samples of the minority class and remove the samples of the majority class. Thus the proposed rough-fuzzy approach performs both oversampling and undersampling to handle the imbalanced class problem. The experimental results demonstrate that the novel technique allows qualitative and quantitative data handling.

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Section: Discussionmentioning

confidence: 99%

Rough-Fuzzy Based Synthetic Data Generation Exploring Boundary Region of Rough Sets to Handle Class Imbalance Problem

Naushin

Das

Nayak

et al. 2023

Axioms

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“…In the case of a small number of points, an augmentation can be made. The usual way to create new points is by interpolation [25,26]. The idea is to find a new potential point between at least two others.…”

Section: Bathymetric Pointsmentioning

confidence: 99%

Spatial Visualization Based on Geodata Fusion Using an Autonomous Unmanned Vessel

et al. 2023

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The visualization of riverbeds and surface facilities on the banks is crucial for systems that analyze conditions, safety, and changes in this environment. Hence, in this paper, we propose collecting, and processing data from a variety of sensors—sonar, LiDAR, multibeam echosounder (MBES), and camera—to create a visualization for further analysis. For this purpose, we took measurements from sensors installed on an autonomous, unmanned hydrographic vessel, and then proposed a data fusion mechanism, to create a visualization using modules under and above the water. A fusion contains key-point analysis on classic images and sonars, augmentation/reduction of point clouds, fitting data and mesh creation. Then, we also propose an analysis module that can be used to compare and extract information from created visualizations. The analysis module is based on artificial intelligence tools for the classification tasks, which helps in further comparison to archival data. Such a model was tested using various techniques to achieve the fastest and most accurate visualizations possible in simulation and real case studies.

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