On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
type: Item
instance of: scholarly article
title: On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis
zbMATH Open document ID: 1475.68285
author: Vitalik Melnikov
author: Eyke Hüllermeier
published in: Machine Learning
publication date: 2018-12-07T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68T20
zbMATH DE Number: 6990193
zbMATH Keywords: nested dichotomies
zbMATH Keywords: multi-class classification
zbMATH Keywords: decomposition method
describes a project that uses: OpenML
describes a project that uses: Auto-WEKA
describes a project that uses: Scikit
describes a project that uses: auto-sklearn
MaRDI profile type: Publication
full work available at URL: s10994-018-5733-1
OpenAlex ID: W2809939257
cites work: Q4386929
cites work: An investigation of missing data methods for classification trees applied to binary response data
cites work: The generation of random, binary unordered trees
cites work: 10.1162/153244302320884605
cites work: Q5396710
cites work: Q3093184
cites work: Numbering binary trees with labeled terminal vertices
cites work: Q4236280
Wikidata QID: Q129617423
DOI: 10.1007/S10994-018-5733-1
description: scientific article; zbMATH DE number 6990193