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Approcci di apprendimento automatico per la previsione dei difetti del software

Approcci di apprendimento automatico per la previsione dei difetti del software


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About the Book

Nel mondo odierno basato sui dati, la maggior parte delle applicazioni reali deve affrontare la sfida dello squilibrio delle classi, in cui i dati critici sono rari. Questo squilibrio influisce gravemente sull'accuratezza dei modelli di classificazione, specialmente in ambiti sensibili come la diagnosi medica, la finanza e la previsione dei difetti del software. Nei sistemi software, la previsione precoce dei difetti è essenziale per ridurre i costi e migliorare l'affidabilità. Tuttavia, molti modelli di apprendimento automatico falliscono a causa di set di dati distorti. Per affrontare questo problema, proponiamo tre nuovi algoritmi: IDROS (sovracampionamento), IDRUS (sottocampionamento) e un approccio ibrido IDROSUS. IDROS utilizza KNN attorno al centroide minoritario per generare dati sintetici, mentre IDRUS rimuove i campioni maggioritari meno rilevanti in base alla distanza dalla media. L'approccio ibrido IDROSUS bilancia contemporaneamente entrambi i lati, riducendo l'overfitting e l'underfitting. Abbiamo valutato questi metodi utilizzando 40 set di dati provenienti dal repository PROMISE e li abbiamo testati su otto classificatori. Metriche di prestazione come accuratezza, richiamo, precisione e misura F hanno dimostrato che IDROSUS ha superato le tecniche esistenti.


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Product Details
  • ISBN-13: 9786209202124
  • Publisher: Edizioni Sapienza
  • Publisher Imprint: Edizioni Sapienza
  • Height: 229 mm
  • No of Pages: 80
  • Returnable: N
  • Weight: 172 gr
  • ISBN-10: 6209202128
  • Publisher Date: 30 Oct 2025
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Spine Width: 5 mm
  • Width: 152 mm


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