Abstract
Handwriting is individualistic where it presents various types of features represent the writer’s characteristics. Not all the features are relevant for Writer Identification (WI) process and some are irrelevant. Removing these irrelevant features called as feature selection process. Feature selection select only the importance features and can improve the classification accuracy. This chapter investigated feature selection process using tree-base structure method in WI domain. Tree-base structure method able to generate a compact subset of non-redundant features and hence improves interpretability and generalization. Random forest (RF) of tree-base structure method is used for feature selection method in WI. An experiment is carried out using image dataset from IAM Hand-writing Database. The results show that RF tree successively selects the most significant features and gives good classification performance as well.
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Acknowledgments
This work was funded by the Ministry of Higher Education Malaysia and Universiti Teknikal Malaysia Melaka (UTeM) through the Fundamental Research Grant Scheme—FRGS/2/2013/ICT02/FTMK/02/4/F00187.
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Sukor, N.A., Muda, A.K., Muda, N.A., Choo, YH., Goh, O.S. (2015). Tree-base Structure for Feature Selection in Writer Identification. In: Abraham, A., Muda, A., Choo, YH. (eds) Pattern Analysis, Intelligent Security and the Internet of Things. Advances in Intelligent Systems and Computing, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-17398-6_19
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DOI: https://doi.org/10.1007/978-3-319-17398-6_19
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