PSO and Computationally Inexpensive Sequential Forward Floating Selection in acquiring significant features for handwritten authorship
Published 1 December 2011
Satrya Fajri Pratama, Azah Kamilah Muda, Yun‐Huoy Choo, Noor Azilah Muda
Citations12
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TL;DR
A hybrid feature selection method of Particle Swarm Optimization and Computationally Inexpensive Sequential Forward Floating Selection for Writer Identification is proposed and the promising applicability has been demonstrated.
Abstract
The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification in order to find the most significant features. This paper proposes a hybrid feature selection method of Particle Swarm Optimization and Computationally Inexpensive Sequential Forward Floating Selection for Writer Identification. The promising applicability of the proposed method has been demonstrated and worth to receive further exploration in identifying the handwritten authorship.
Keywords
Computer ScienceEngineering
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