A hybrid approach for automatic model recommendation

Roman Vainshtein, Asnat Greenstein-Messica, Gilad Katz, Bracha Shapira, Lior Rokach

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

13 Scopus citations

Abstract

One of the challenges of automating machine learning applications is the automatic selection of an algorithmic model for a given problem. We present AutoDi, a novel and resource-efficient approach for model selection. Our approach combines two sources of information: metafeatures extracted from the data itself and word-embedding features extracted from a large corpus of academic publications. This hybrid approach enables AutoDi to select top-performing algorithms both for widely and rarely used datasets by utilizing its two types of feature sets. We demonstrate the effectiveness of our proposed approach on a large dataset of 119 datasets and 179 classification algorithms grouped into 17 families. We show that AutoDi can reach an average of 98.8% of optimal accuracy and select the optimal classification algorithm in 49.5% of all cases.

Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages1623-1626
Number of pages4
ISBN (Electronic)9781450360142
DOIs
StatePublished - 17 Oct 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Country/TerritoryItaly
CityTorino
Period22/10/1826/10/18

Keywords

  • Algorithm Recommendation
  • Classification
  • Classifier Families
  • Dataset Meta-features
  • Expert System
  • Meta-Learning
  • Scholarly Big Data
  • Word Embedding

ASJC Scopus subject areas

  • Decision Sciences (all)
  • Business, Management and Accounting (all)

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