Abstract
The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.
Highlights
The classication is one of the problems in data mining and its task is to predict the class for previously unseen data after learning patterns or relationships from data with known classes
For example Liu et al.[2] considered the parallelizing Gene Expression Programming (GEP) algorithm to enable large-scale classication, using majority-voting to combine a number of GEP-based classiers obtained for separate data chunks
In Ref. 6, the authors proposed a GEP-based batch ensemble classier that uses stacked generalization concept, in which the higher-level model is created from lower level classiers in the form of a meta-gene
Summary
The classication is one of the problems in data mining and its task is to predict the class for previously unseen data after learning patterns or relationships from data with known classes. Gene Expression Programming (GEP) has been used as an e®ective classication algorithm. This is an Open Access article published by World Scientic Publishing Company. For example Liu et al.[2] considered the parallelizing GEP algorithm to enable large-scale classication, using majority-voting to combine a number of GEP-based classiers obtained for separate data chunks. J»edrzejowicz and J»edrzejowicz in a series of papers[3,4,5,6] proposed several approaches to combining expression trees induced by GEP applied to random subsets of the original data. In Ref. 6, the authors proposed a GEP-based batch ensemble classier that uses stacked generalization concept, in which the higher-level model is created from lower level classiers in the form of a meta-gene.
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