Affiliations: Research Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
Correspondence:
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Corresponding author: Naeimeh Elkhani, Research Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. E-mail:[email protected]
Abstract: Cancer is the main public health issue in most places of
the world due to the difficulties in its early diagnosis and to begin early
treatment. In the recent years many techniques have been proposed to tackle
the high dimensionality in microarray cancer datasets. This paper proposed a
membrane-inspired feature selection method to utilize the potentials of
membrane computing. In this regard, Kernel P system is modelled based on
multi objective binary particle swarm optimization feature selection method
through nine modeling steps briefly involving objects of compartments,
labels of compartments, multiset of objects in the environment, system
structure, and rules involved in each compartment. The proposed model
evaluated by cell line data of colorectal cancer. As the initial attempt to
come out with a model, division rule and sequential computation on Matlab
are used as tools to define potential of membrane computing in trading space
against time computation. The evaluation results through Weka indicate that
selected marker genes through the proposed model generate high
classification accuracy, precision, and recall metrics in compared with pure
multi objective binary particle swarm optimization feature selection.
Keywords: Membrane computing, microarray cancer data, Kernel P system, feature selection, multi objective binary particle swarm optimization feature selection
