Merging intelligent API responses using a proportional representation approach

Tomohiro Ohtake, Alex Cummaudo, Mohamed Abdelrazek, Rajesh Vasa, John Grundy

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationWeb Engineering
Subtitle of host publication19th International Conference, ICWE 2019 Daejeon, South Korea, June 11–14, 2019 Proceedings
EditorsMaxim Bakaev, Flavius Frasincar, In-Young Ko
Place of PublicationCham Switzerland
Number of pages16
ISBN (Electronic)9783030192747
ISBN (Print)9783030192730
Publication statusPublished - 2019
EventInternational Conference on Web Engineering 2019 - Daejeon, Korea, Republic of (South)
Duration: 11 Jun 201914 Jun 2019
Conference number: 19th

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Web Engineering 2019
Abbreviated titleICWE 2019
CountryKorea, Republic of (South)
Internet address


  • Application programming interfaces
  • Artificial intelligence
  • Data integration
  • Supervised learning
  • Web services

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