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Merging intelligent API responses using a proportional representation approach

conference contribution
posted on 01.01.2019, 00:00 authored by T Ohtake, Alex CummaudoAlex Cummaudo, Mohamed AbdelrazekMohamed Abdelrazek, Rajesh VasaRajesh Vasa, John Grundy
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.

History

Event

International Society for the Web Engineering. Conference (19th : 2019 : Daejeon, South Korea)

Volume

11496

Series

International Society for the Web Engineering Conference

Pagination

391 - 406

Publisher

Springer

Location

Daejeon, South Korea

Place of publication

Cham, Switzerland

Start date

11/06/2019

End date

14/06/2019

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030192730

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2019, Springer Nature Switzerland AG

Editor/Contributor(s)

M Bakaev, F Frasincar, I Ko

Title of proceedings

ICWE 2019 : Proceedings of the 19th International Conference on Web Engineering 2019