Analisi della classificazione per dati sbilanciati
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Analisi della classificazione per dati sbilanciati

Analisi della classificazione per dati sbilanciati


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About the Book

Un'enorme quantità di dati viene raccolta e archiviata nei database di tutto il mondo. Questi dati si accumulano e continuano ad aumentare ogni anno. Estrarre le informazioni nascoste in questi database e classificare le informazioni estratte sono i compiti più importanti del data mining. Se questi insiemi di dati sono sbilanciati, diventa difficile gestirli. La previsione del futuro è uno dei compiti fondamentali del data mining. Lavorare con insiemi di dati sbilanciati per prevedere i possibili risultati è un compito molto noioso. Un set di dati è sbilanciato quando non viene classificato correttamente, quando una classe contiene più istanze di altre. Spesso vengono rappresentate come una classe positiva (minoranza) e una negativa (maggioranza). La classe che ha un numero minore di campioni è chiamata classe minoritaria, mentre quella che ne ha di più è chiamata classe maggioritaria. I dataset sbilanciati causano molti problemi seri nel data mining, per lo più l'algoritmo di classificazione standard considera il dataset come bilanciato, che a sua volta è parziale verso la classe maggioritaria. Per applicazioni come la diagnosi medica, ciò provoca effetti molto gravi. Il bilanciamento del set di dati è quindi fondamentale per molte applicazioni in tempo reale.


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Product Details
  • ISBN-13: 9786206345459
  • Publisher: KS Omniscriptum Publishing
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Weight: 163 gr
  • ISBN-10: 6206345459
  • Publisher Date: 16 Aug 2023
  • Height: 229 mm
  • No of Pages: 68
  • Spine Width: 4 mm
  • Width: 152 mm


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