GAssist vs. BioHEL: Critical assessment of two paradigms of genetics-based machine learning

Soft Computing

Volumn 17, Issue 6, 2013, Pages 953-981

  Franco, María A ^a   Krasnogor, Natalio ^a   Bacardit, Jaume ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Classification; Comparison methodologies; Data mining; Genetics based machine learning; Sensitivity and scalability analysis

Indexed keywords

COMPARISON METHODOLOGIES; CRITICAL ASSESSMENT; GENETICS BASED MACHINE LEARNING; ITERATIVE RULE LEARNING; LEARNING CAPABILITIES; LEARNING PARADIGMS; PITTSBURGH APPROACH; SCALABILITY ANALYSIS;

CHROMOSOMES; CLASSIFICATION (OF INFORMATION); DATA MINING; ITERATIVE METHODS; LEARNING SYSTEMS;

LEARNING ALGORITHMS;

EID: 84877790945     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-013-1016-8     Document Type: Article

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