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POB2 Seminar: Instance selection methods: past, present and future
This time we will have the opportunity to listen to the presentation of dr Álvar Arnaiz from University of Burgos, Spain.
The title of the presentation will be: Instance selection methods: past, present and future
We cordially invite you to the POB2 Seminar on May 26, 2022, 15:00.
This time we will have the opportunity to listen to the presentation of dr Álvar Arnaiz from University of Burgos, Spain.
The title of the presentation will be: Instance selection methods: past, present and future
Description:
Instance selection attempts to find the most representative subset, of the initial training data set, without lessening the predictive capabilities of original one. In other words, if we train one algorithm with the original data set, and another with the selected subset, both algorithms must perform in a similar manner. Instance selection can be seen as a special case of instance generation, where the instances to be generated are limited to the original ones. These methods play a central role in data reduction processes. When real-world data sets are examined, the imperative need for instance selection algorithms becomes increasingly clear. On the one hand, the average data set size is becoming larger and larger. On the other hand, real data sets usually contain noisy instances, outliers, and anomalies.
The first instance selection methods emerged half a century ago, since then several algorithms have been presented: for classification, for regression, for multi-label... This talk is about instance selection methods, their beginning, their achievements along the time and the future research lines that are still opened.
The link to the seminar:
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