Learning from multiple data sets with different missing attributes and privacy policies: Parallel distributed fuzzy genetics-based machine learning approach

This paper discusses parallel distributed genetics-based machine learning (GBML) of fuzzy rule-based classifiers from multiple data sets. We assume that each data set has a similar but different set of attributes. In other words, each data set has different missing attributes. Our task is the design...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Ishibuchi, Hisao, Yamane, Masakazu, Nojima, Yusuke
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Classification algorithms Data privacy Distributed databases Evolutionary algorithms fuzzy rule-based classifiers Fuzzy sets genetics-based machine learning horizontally partitioned data sets parallel distributed implementation Sociology Statistics Training data
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