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UID:pretalx-europython-2023-WAZGKA@programme.europython.eu
DTSTART;TZID=CET:20230719T130000
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DESCRIPTION:Multilabel classification is a machine learning task in which e
 ach instance is assigned to a group of labels. It has gained widespread us
 e in various applications in recent years. Preprocessing\, such as feature
  selection\, is an important step in any machine learning or data mining t
 ask. It helps to improve the performance of an algorithm and reduce comput
 ational time by eliminating highly correlated\, irrelevant\, and noisy fea
 tures. A new algorithm called Black Hole\, inspired by the phenomenon of b
 lack holes\, has recently been developed to tackle multi-label classificat
 ion problems. In this talk\, we present a modified version of the Black Ho
 le algorithm that combines it with two genetic algorithm operators: crosso
 ver and mutation. The combination of Black Hole and genetic algorithms has
  the potential to solve multi-label classification problems across a range
  of domains.
DTSTAMP:20260310T181955Z
LOCATION:Terrace 2A
SUMMARY:From Dataset to Features: A Python-Based Evolutionary Approach - Ne
 eraj Pandey\, Hitesh Khandelwal
URL:https://programme.europython.eu/europython-2023/talk/WAZGKA/
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