Mean based relief: An improved feature selection method based on ReliefF

Aggarwal, Nitisha; Shukla, Unmesh; Saxena, Geetika Jain; Rawat, Mukesh; Bafila, Anil Singh; Singh, Sanjeev; Pundir, Amit

doi:10.1007/s10489-023-04662-w

Cited by 13 publications

(1 citation statement)

References 38 publications

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“…Using these features, a cost-effective and fast system performance can be achieved, while reducing overfitting and enhancing interpretability [51]. A univariate feature selection method, ANOVA, and a multivariant feature selection method, MRMR, are applied in this study to assess the significance of the separate features, filter out the irrelevant features and get the optimal feature combination, and evaluate the differences between the two classes [52,53]. The result of the ANOVA test is given in Table 3.…”

Section: Feature Selectionmentioning

confidence: 99%

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Chowa,

Azam,

Montaha

et al. 2024

j Imaging. Inform. med.

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This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature’s significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.

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Section: Feature Selectionmentioning

confidence: 99%