Recurrent Expansion Algorithm for Classification

版本 1.0.0 (73.8 KB) 作者: BERGHOUT Tarek
These codes are designed to explain how the Multiverse Recurrent Expansion with Multiple Repeats (MV-REMR) algorithm works.
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These codes are designed to explain how the Multiverse Recurrent Expansion with Multiple Repeats (MV-REMR) algorithm works. The MV-REMR algorithm is initially presented in [1]. However, for further details, it is recommended to consult other references [2]–[5]. The current codes are dedicated to classification. This example uses a small-sized dataset prepared for illustration, which is taken from realistic case scenarios that have been well-processed. Unfortunately, specific details cannot be addressed in this case due to data privacy concerns. To better understand the working mechanism, it is essential to first read related papers on the algorithm. Subsequently, within each function from these codes, there are enough details provided for a comprehensive understanding.
[1] T. Berghout, M. Benbouzid, and M. A. Ferrag, “Multiverse Recurrent Expansion With Multiple Repeats: A Representation Learning Algorithm for Electricity Theft Detection in Smart Grids,” IEEE Trans. Smart Grid, vol. 14, no. 6, pp. 4693–4703, Nov. 2023, doi: 10.1109/TSG.2023.3250521.
[2] T. Berghout, M. Benbouzid, and M. A. Ferrag, “Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids,” in IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2022, pp. 1–6, doi: 10.1109/IECON49645.2022.9968378.
[3] T. Berghout, M. Benbouzid, and Y. Amirat, “Improving Small-scale Machine Learning with Recurrent Expansion for Fuel Cells Time Series Prognosis,” in IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2022, pp. 1–5, doi: 10.1109/IECON49645.2022.9968566.
[4] T. Berghout and M. Benbouzid, “What Are Recurrent Expansion Algorithms? Exploring a Deeper Space than Deep Learning,” IOCMA 2023 1st Int. Online Conf. Math. Appl. (01-15 May 2023), p. 10, Apr. 2023, doi: 10.3390/IOCMA2023-14387.
[5] T. Berghout, M. Benbouzid, T. Bentrcia, Y. Amirat, and L. Mouss, “Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis,” Entropy, vol. 24, no. 7, p. 1009, Jul. 2022, doi: 10.3390/e24071009.
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MV_REMR_Codes_Class

MV_REMR_Codes_Class/Functions

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1.0.0