Instance spaces for machine learning classification

DOI10.1007/s10994-017-5629-5zbMath1457.68235OpenAlexW2775947831WikidataQ62033260 ScholiaQ62033260MaRDI QIDQ1707470

Davaatseren Baatar, Laura Villanova, Mario Andrés Muñoz, Kate A. Smith-Miles

Publication date: 3 April 2018

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-017-5629-5

zbMATH Keywords

classificationperformance evaluationinstance spacemeta-learninginstance difficultytest dataalgorithm footprintstest instance generation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (11)

A new class of hard problem instances for the 0-1 knapsack problem ⋮ Enhanced instance space analysis for the maximum flow problem ⋮ Instance space analysis and algorithm selection for the job shop scheduling problem ⋮ The international timetabling competition on sports timetabling (ITC2021) ⋮ AutonoML: Towards an Integrated Framework for Autonomous Machine Learning ⋮ Features for the 0-1 knapsack problem based on inclusionwise maximal solutions ⋮ On normalization and algorithm selection for unsupervised outlier detection ⋮ Revisiting \textit{where are the hard knapsack problems?} Via instance space analysis ⋮ Instance space analysis for a personnel scheduling problem ⋮ Relating instance hardness to classification performance in a dataset: a visual approach ⋮ Evolving test instances of the Hamiltonian completion problem

Uses Software

• PYTHIA
• R
• UCI-ml
• MASS (R)
• kknn
• JStatCom
• WEKA
• KEEL
• rpart
• Kernlab
• e1071
• ElemStatLearn
• OpenML
• GAUSS
• UCI++
• ReliefF

Cites Work

• Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
• Towards objective measures of algorithm performance across instance space
• Generating new test instances by evolving in instance space
• Discovering the suitability of optimisation algorithms by learning from evolved instances
• The changing science of machine learning
• On the handling of continuous-valued attributes in decision tree generation
• Theoretical and empirical analysis of ReliefF and RReliefF
• Measuring instance difficulty for combinatorial optimization problems
• A no-free-lunch theorem for non-uniform distributions of target functions
• GAUSS: an online algorithm selection system for numerical quadrature
• Very simple classification rules perform well on most commonly used datasets
• Machine Learning
• Machine learning: Trends, perspectives, and prospects
• Experiment Databases
• Statistical Analysis of Financial Data in S-Plus
• Metalearning
• PYTHIA
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