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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.06212 |
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| _version_ | 1866916316153118720 |
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| author | Gubbels, Luuk Puts, Marco Daas, Piet |
| author_facet | Gubbels, Luuk Puts, Marco Daas, Piet |
| contents | Online platform businesses can be identified by using web-scraped texts. This is a classification problem that combines elements of natural language processing and rare event detection. Because online platforms are rare, accurately identifying them with Machine Learning algorithms is challenging. Here, we describe the development of a Machine Learning-based text classification approach that reduces the number of false positives as much as possible. It greatly reduces the bias in the estimates obtained by using calibrated probabilities and ensembles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bias Correction in Machine Learning-based Classification of Rare Events Gubbels, Luuk Puts, Marco Daas, Piet Machine Learning Online platform businesses can be identified by using web-scraped texts. This is a classification problem that combines elements of natural language processing and rare event detection. Because online platforms are rare, accurately identifying them with Machine Learning algorithms is challenging. Here, we describe the development of a Machine Learning-based text classification approach that reduces the number of false positives as much as possible. It greatly reduces the bias in the estimates obtained by using calibrated probabilities and ensembles. |
| title | Bias Correction in Machine Learning-based Classification of Rare Events |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.06212 |