Merging Intelligent API Responses Using a Proportional Representation Approach

Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs...

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Bibliographic Details
Published inWeb Engineering pp. 391 - 406
Main Authors Ohtake, Tomohiro, Cummaudo, Alex, Abdelrazek, Mohamed, Vasa, Rajesh, Grundy, John
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Application programming interfaces
Artificial intelligence
Data integration
Supervised learning
Web services
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Summary:Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the proportional representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional proportional representation methods by 0.015 F-measure.
ISBN:9783030192730
3030192733
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-19274-7_28