Relating instance hardness to classification performance in a dataset: a visual approach
type: Item
instance of: scholarly article
title: Relating instance hardness to classification performance in a dataset: a visual approach
author: Pedro Yuri Arbs Paiva
author: C. Moreno
author: Kate A. Smith-Miles
author: Maria Gabriela Valeriano
author: Ana Carolina Lorena
published in: Machine Learning
publication date: 2022-11-28T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7624266
zbMATH Keywords: instance hardness
zbMATH Keywords: classification performance
zbMATH Keywords: hardness embedding
zbMATH Keywords: meta-learning
describes a project that uses: Hyperopt
describes a project that uses: MATILDA
describes a project that uses: Spearmint
describes a project that uses: PyISpace
describes a project that uses: Python
MaRDI profile type: Publication
full work available at URL: s10994-022-06205-9
OpenAlex ID: W4283318985
cites work: On normalization and algorithm selection for unsupervised outlier detection
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cites work: Revisiting \textit{where are the hard knapsack problems?} Via instance space analysis
cites work: Class noise vs. attribute noise: A quantitative study of their impacts
DOI: 10.1007/S10994-022-06205-9
description: scientific article; zbMATH DE number 7624266