Towards Achieving Optimal Performance using
Stacked Generalization Algorithm: A Case
Study of Clinical Diagnosis of Malaria Fever
Abiodun
Oguntimilehin1, Olusola Adetunmbi2, and Innocent Osho3
1Department
of Computer Science, Afe Babalola University, Ado-Ekiti
2Department of Computer Science, Federal
University of Technology, Akure
3Department of Animal Production and
Health, Federal University of Technology, Akure
Abstract: The birth of
data mining has been a blessing to all fields of endeavours and there are
numerous data mining algorithms available today. One of the major problems of
mining data is the selection of the appropriate algorithm or model for a job at
hand; this has led to different comparison experiments by researchers. Stacked
Generalization is one of the methods of combining multiple models to give a
better accuracy. The method has been investigated to be effective by many
researchers over the years. This study investigates how optimal performance
could be achieved using Stacked Generalization algorithm. Six different data
mining algorithms (PART, REP Tree, J48, Random Tree, RIDOR and JRIP) arranged
in two different orders were used as base learners to two different Meta
Learners (Random Forest and NNGE) independently and the results obtained were
compared in terms of classification accuracy. The study shows that the order of
arrangement of the base learners and the choice of Meta Learner could affect
the accuracy of the Stacked Generalization method; NNGE outperforms Random
Forest as a Meta-Learner and its performance is independent of the order of
arrangement of the base learners as against Random Forest. Malaria fever
datasets collected from reputable hospitals in Ado-Ekiti, Ekiti State, Nigeria
were purposefully used for this study because malaria is one of the major
diseases killing almost a million people yearly in the tropical region of
Africa, so a more accurate malaria fever diagnosis model is as well proposed as
a result of this study.
Keywords: Data
mining, ensemble learning, stacked generalization, malaria, diagnosis.
Received October 13, 2016; accepted October 11, 2017