An Evaluation of Feature Selection Methods on Multi-Class Imbalance and High Dimensionality Shape-Based Leaf Image Features

Authors

  • Mohd Shamrie Sainin Computational Intelligence Research Group, School of Computing, College of Arts and Sciences, Universiti Utara Malaysia.
  • Rayner Alfred Faculty of Computing and Informatics, Universiti Malaysia Sabah
  • Faudziah Ahmad Computational Intelligence Research Group, School of Computing, College of Arts and Sciences, Universiti Utara Malaysia.
  • Mohamed A.M. Lammasha Computational Intelligence Research Group, School of Computing, College of Arts and Sciences, Universiti Utara Malaysia.

Keywords:

Feature Selection, Multiclass Imbalance, High Dimensionality, Leaf,

Abstract

Multi-class imbalance shape-based leaf image features requires feature subset that appropriately represent the leaf shape. Multi-class imbalance data is a type of data classification problem in which some data classes is highly underrepresented compared to others. This occurs when at least one data class is represented by just a few numbers of training samples known as the minority class compared to other classes that make up the majority class. To address this issue in shapebased leaf image feature extraction, this paper discusses the evaluation of several methods available in Weka and a wrapperbased genetic algorithm feature selection.

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Published

2017-03-01

How to Cite

Sainin, M. S., Alfred, R., Ahmad, F., & Lammasha, M. A. (2017). An Evaluation of Feature Selection Methods on Multi-Class Imbalance and High Dimensionality Shape-Based Leaf Image Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-2), 57–61. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1656

Issue

Vol. 9 No. 1-2: Advancing Digital Societies for Social and Cultural Improvement

Section

Articles

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