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Keywords = automatic invoice labeling

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22 pages, 727 KB  
Article
Semi-Supervised Classification with A*: A Case Study on Electronic Invoicing
by Bernardo Panichi and Alessandro Lazzeri
Big Data Cogn. Comput. 2023, 7(3), 155; https://doi.org/10.3390/bdcc7030155 - 20 Sep 2023
Cited by 1 | Viewed by 3342
Abstract
This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine [...] Read more.
This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine learning holds promise for such repetitive tasks, the presence of low-quality training data often poses a hurdle. Frequently, labels pertain to invoice rows at a group level rather than an individual level, leading to the exclusion of numerous records during preprocessing. To enhance the efficiency of an invoice entry classifier within a semi-supervised context, this study proposes an innovative approach that combines the classifier with the A* graph search algorithm. Through experimentation across various classifiers, the results consistently demonstrated a noteworthy increase in accuracy, ranging between 1% and 4%. This improvement is primarily attributed to a marked reduction in the discard rate of data, which decreased from 39% to 14%. This paper contributes to the literature by presenting a method that leverages the synergy of a classifier and A* graph search to overcome challenges posed by limited and group-level label information in the realm of electronic invoicing classification. Full article
(This article belongs to the Special Issue Computational Finance and Big Data Analytics)
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