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2 Annals of Computer Science and Information Systems

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Rotation Invariance in Graph Convolutional Networks

100%

Mac N. A. , Nguyen H. S.

Annals of Computer Science and Information Systems

2021

tom Vol. 25

81--90

Convolution filters in deep convolutional networks display rotation variant behavior. While learned invariant behavior can be partially achieved, this paper shows that current methods of utilizing rotation variant features can be improved by proposing a grid-based graph convolutional network. We demonstrate that Grid-GCN heavily outperforms existing models on rotated images, and through a set of ablation studies, we show how the performance of Grid-GCN implies that there exist more performant methods to utilize fundamentally rotation variant features and we conclude that the inherit nature of spectral graph convolutions is able to learn invariant behavior.

Feature Selection and Ranking Method based on Intuitionistic Fuzzy Matrix and Rough Sets

100%

Vo B. K. , Nguyen H. S.

Annals of Computer Science and Information Systems

2022

tom Vol. 30

279--288

In this paper we propose a novel rough-fuzzy hybridization technique to feature selection and feature ranking problem. The idea is to model the local preference relation between pair of features by intuitionistic fuzzy values and search for a feature ranking that is consistent with those constraints. We apply the techniques used in group decision making where constraints are presented in form of intuitionistic fuzzy preference relation. The proposed method has been illustrated by some simple examples.