Abstract
Feature subset selection is an important data reduction technique. Effects of feature selection on classifier’s accuracy are extensively studied yet comprehensibility of the resultant model is given less attention. We show that a weak feature selection method may significantly increase the complexity of a classification model. We also proposed an extendable feature selection methodology based on our preliminary results. Insights from the study can be used for developing clinical decision support systems.
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Acknowledgments
This work was supported by the Industrial Core Technology Development Program (10049079, Develop of mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) and This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2014R1A2A2A01003914.
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Ali, S.I., Kang, B.H., Lee, S. (2016). Application of Feature Subset Selection Methods on Classifiers Comprehensibility for Bio-Medical Datasets. In: GarcÃa, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_4
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DOI: https://doi.org/10.1007/978-3-319-48746-5_4
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