SOCIFS feature selection framework for handwritten authorship

Authors:
Satrya Fajri Pratama;Azah Kamilah Muda;Yun-Huoy Choo;Noor Azilah Muda
Affiliations:
Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia;Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia;Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia;Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, University Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2013

Citing 12
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Abstract

The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. This paper is meant to propose a novel feature selection framework for Swarm Optimized and Computationally Inexpensive Floating Selection SOCIFS, by exploring existing feature selection frameworks, and compare the performance of proposed feature selection framework against various feature selection methods in Writer Identification in order to find the most significant features. The promising applicability of the proposed framework has been demonstrated in the result and worth to receive further exploration in identifying the handwritten authorship.