GCN embedded with Polynomial Aggregation Function for Group Activity Recognition

Hemantkumar, Panchal Raj; Gupta, Tanvi; Vashisth, Anjali; Chintha, Prithvi; Wargiya, Aayush Vijay; George, Mathew Gigi; Alexander, Meyga Anne; Narayanan, Swathi Jamjala

doi:10.1109/cict51604.2020.9312103

2020 IEEE 4th Conference on Information &Amp; Communication Technology (CICT) 2020

DOI: 10.1109/cict51604.2020.9312103

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GCN embedded with Polynomial Aggregation Function for Group Activity Recognition

Panchal Raj Hemantkumar

Tanvi Gupta

Anjali Vashisth

et al.

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2023

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“…Special classes of aggregation functions are distinguished in the literature. These include t-norms [12], [13], OWA operators [14], [15], fuzzy measures [16], polynomial function [17], order-2 fuzzy sets [18], or fuzzy generalized unified aggregation operator [19]. There are also granular models, see [20], [21].…”

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confidence: 99%

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

Karczmarek,

Dolecki,

Powroznik

et al. 2023

IEEE Access

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Correct classification remains a challenge for researchers and practitioners developing algorithms. Even a minor enhancement in classification quality, for instance, can significantly boost the effectiveness of detecting conditions or anomalies in safety data. One solution to this challenge involves aggregating classification results. This process can be executed effectively as long as the aggregation function is appropriately chosen. One of the most efficient aggregation operators is the Choquet integral. Furthermore, there exist numerous generalizations and extensions of the Choquet integral in the existing literature. In this study, we conduct a comprehensive analysis and evaluation of a novel approach for deriving an aggregate classification. The aggregation process applied to various classifiers is based on enhancements to the Choquet integral. These novel expressions draw inspiration from Newton-Cotes quadratures and other well-known formulae from numerical analysis. In contrast to previous approaches that exploit the generalization of the Choquet integral, our approach requires the utilization of two or three adjacent values associated with the membership of a specific element in different classes. This enables the use of more efficient enhancements in terms of accuracy measurement. Specifically, the t-norm following the integral symbol can be effectively replaced by mathematical expressions used in executing numerical integration formulae. This leads to more precise results and aligns with the concept of numerical integration. Furthermore, in a series of experiments, we thoroughly assess the performance of the proposed approach in terms of classification accuracy. We analyze the strengths and weaknesses of the new approach and establish the experimental settings that can be applied to similar tasks. In the series of experiments, we have demonstrated that the proposed Quadrature-Inspired Generalized Choquet Integral (QIGCI) can either outperform previous enhancements of the Choquet integral or at least achieve a similar level of accuracy measurement. However, we also highlight scenarios where previous approaches can still be a suitable choice. The number of QIGCI-based aggregation models that outperform others is convincing, indicating that this approach is worthy of consideration. INDEX TERMSAggregation, anomaly detection, Choquet integral, generalized Choquet integral, numerical quadratures, many classifiers, pre-aggregation functions I. INTRODUCTION T HE The problem of classification has recently become one of the most significant challenges in theoretical and applied computer science. It arises in various contexts such as image analysis, anomaly detection, and tabular data recognition. Especially in the past decade, this task has seen

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confidence: 99%

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

Karczmarek,

Dolecki,

Powroznik

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

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GCN embedded with Polynomial Aggregation Function for Group Activity Recognition

Cited by 1 publication

References 10 publications

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

Quadrature-Inspired Generalized Choquet Integral in an Application to Classification Problems

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