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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2307.00106 |
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| _version_ | 1866914426041401344 |
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| author | Barboza, Eduardo V. L. de Almeida, Paulo R. Lisboa Britto Jr, Alceu de Souza Cruz, Rafael M. O. |
| author_facet | Barboza, Eduardo V. L. de Almeida, Paulo R. Lisboa Britto Jr, Alceu de Souza Cruz, Rafael M. O. |
| contents | Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-known distance functions in data streams without normalization, normalized considering the statistics of the first batch of data received, and considering the previous batch received. We argue that experimental protocols for streams that consider the full stream as normalized are unrealistic and can lead to biased and poor results. Our results indicate that using the original data stream without applying normalization, and the Canberra distance, can be a good combination when no information about the data stream is known beforehand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00106 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distance Functions and Normalization Under Stream Scenarios Barboza, Eduardo V. L. de Almeida, Paulo R. Lisboa Britto Jr, Alceu de Souza Cruz, Rafael M. O. Machine Learning Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-known distance functions in data streams without normalization, normalized considering the statistics of the first batch of data received, and considering the previous batch received. We argue that experimental protocols for streams that consider the full stream as normalized are unrealistic and can lead to biased and poor results. Our results indicate that using the original data stream without applying normalization, and the Canberra distance, can be a good combination when no information about the data stream is known beforehand. |
| title | Distance Functions and Normalization Under Stream Scenarios |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.00106 |