Machine Learning in Social Networks

Aggarwal, Manasvi; Murty, M. Narasimha

doi:10.1007/978-981-33-4022-0

Cited by 12 publications

(1 citation statement)

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“…The goal of backpropagation is to optimize the network parameter for arbitrarily mapping inputs to the supervisory labels in successive iterations of the learning process. [ 61–63 ] For any specific layer ‘l’ of the network, Equations (6)–(7) present the weight update where 𝜂 is the ‘learning rate’ that controls the influence of the current gradient on the weight update. The next section presents the procedure for estimating sample quality adopted from Chen and Ge.…”

Section: The Robust Neural Network Modelmentioning

confidence: 99%

A robust neural network model for fault detection in the presence of mislabelled data

Alauddin,

Khan,

Imtiaz

et al. 2024

Can J Chem Eng

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Several data‐driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data‐based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low‐quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low‐quality data issues which are crucial in the data‐driven modelling of processing systems.

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Section: The Robust Neural Network Modelmentioning

confidence: 99%