Feature Encoding For High Cardinality Categorical Variables Using Entity Embeddings: A Case Study in Customs Fraud Detection-Reference-Cited by-同舟云学术

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Feature Encoding For High Cardinality Categorical Variables Using Entity Embeddings: A Case Study in Customs Fraud Detection

Published:2022-10-06 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Cyber Resilience (ICCR)
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Author:

Jiun Hooi Eric Khoo¹,Zainal Anazida¹,Kassim Mohamad Nizam²,Ayub Zaily³

Affiliation:

1. Universiti Teknologi Malaysia,Faculty of Computing,Johor Bahru,Johor,Malaysia,81310

2. Bahagian Penyelidikan Strategik, National Anti-Financial Crime Center,Putrajaya,Malaysia

3. Technical Services Division, Valuation Management Branch,Royal Malaysian Customs Department,Putrajaya,Malaysia

Funder

Universiti Teknologi Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9995718/9995732/09995764.pdf?arnumber=9995764

Reference25 articles.

1. The Tax Gap: A Methodological Review

2. Preliminary update of the tax year 2001 individual income tax underreporting gap estimates;Plumley

3. Customs fraud detection

4. DATE

5. Customs Fraud Detection in the Presence of Concept Drift

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1. Stochastic Target Encoder - A new categorical feature encoding applied to urban data regression problems;Anais do XVI Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP 2024);2024-07-21

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