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This paper presents a study on alpha integration, a method for classifier fusion, and their applications. This method aims to improve the classification performance of multiple classifiers relatively simple from with respect to the least mean squares (LMSE) and the minimum probability of error (MPE) criteria optimization criteria. The state of the art of the design of the method and applications is summarized. Alpha integration shows a wide range of successful real applications including the following: non-destructive testing, credit card fraud detection, and medical diagnosis.

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On Application of Alpha Integration to the Fusion of Multiple Classifiers

  • 会议论文

  • 发表日期：

    2025年

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  • Addisson Salazar,
  • Luis Vergara,
  • Alberto Rodriguez

摘要

This paper presents a study on alpha integration, a method for classifier fusion, and their applications. This method aims to improve the classification performance of multiple classifiers relatively simple from with respect to the least mean squares (LMSE) and the minimum probability of error (MPE) criteria optimization criteria. The state of the art of the design of the method and applications is summarized. Alpha integration shows a wide range of successful real applications including the following: non-destructive testing, credit card fraud detection, and medical diagnosis.