Impact of the characteristics of data sets on incremental learning | Zendy

Patrick Marques Ciarelli | Zendy; Elias Oliveira | Zendy; Evandro Ottoni Teatini Salles | Zendy

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Impact of the characteristics of data sets on incremental learning

Author(s) -

Patrick Marques Ciarelli,

Elias Oliveira,

Evandro Ottoni Teatini Salles

Publication year - 2013

Publication title -

artificial intelligence research

Language(s) - English

Resource type - Journals

eISSN - 1927-6982

pISSN - 1927-6974

DOI - 10.5430/air.v2n4p63

Subject(s) - stability (learning theory) , computer science , task (project management) , set (abstract data type) , artificial neural network , incremental learning , machine learning , artificial intelligence , plasticity , data set , boundary (topology) , class (philosophy) , data mining , pattern recognition (psychology) , mathematics , engineering , mathematical analysis , systems engineering , thermodynamics , programming language , physics

Little attention has been paid to identifying the characteristics of a data set that provide favorable conditions for the task of incremental learning. In this work, several metrics were used to characterize data sets and identify the characteristics that may influence the trade-off between stability and plasticity. Three metrics are proposed for the evaluation of stability, plasticity and the trade-off between them in incremental techniques. The experiments were carried out using four incremental neural networks, and the results showed that the shape of the class boundary and spatial distribution of the samples have a great influence on this trade-off.

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