An evaluation of feature selection technique for dendrite cell algorithm

Mohamad Farhan Mohamad Mohsin, Abdul Razak Hamdan, Azuraliza Abu Bakar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Dendrite cell algorithm needs appropriates feature to represents its specific input signals. Although there are many feature selection algorithms have been used in identifying appropriate features for dendrite cell signals, there are algorithms that never been investigated and limited work to compare performance among them. In this study, six feature selection algorithms namely Information Gain, Gain Ratio, Symmetrical Uncertainties, Chi Square, Support Vector Machine, and Rough Set with Genetic Algorithm Reduct are examined and their effectiveness to represent dendrite cell signal are evaluated. Eight universal datasets are chosen and assessing their performance according to sensitivity, specificity, and accuracy. From the experiment, the Rough Set Genetic Algorithm reduct is found to be the most effect feature selection for dendrite cell algorithm when it generates a consistent result for all evaluation metrics. In single evaluation metrics, the chi square technique has the best competence in term of sensitiveness while the rough set genetic algorithm reduct is good at specificity and accuracy. In the next step, further analysis will be conducted on complex dataset such as time series data set.

Original languageEnglish
Title of host publication2014 International Conference on IT Convergence and Security, ICITCS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479965410
DOIs
Publication statusPublished - 23 Jan 2014
Event4th 2014 International Conference on IT Convergence and Security, ICITCS 2014 - Beijing, China
Duration: 28 Oct 201430 Oct 2014

Other

Other4th 2014 International Conference on IT Convergence and Security, ICITCS 2014
CountryChina
CityBeijing
Period28/10/1430/10/14

Fingerprint

Feature extraction
Genetic algorithms
Support vector machines
Time series
Experiments

Keywords

  • artificial immune system
  • danger theory
  • dendrite cell algorithm
  • feature selection
  • signal selection

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Mohsin, M. F. M., Hamdan, A. R., & Abu Bakar, A. (2014). An evaluation of feature selection technique for dendrite cell algorithm. In 2014 International Conference on IT Convergence and Security, ICITCS 2014 [7021732] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITCS.2014.7021732

An evaluation of feature selection technique for dendrite cell algorithm. / Mohsin, Mohamad Farhan Mohamad; Hamdan, Abdul Razak; Abu Bakar, Azuraliza.

2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7021732.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mohsin, MFM, Hamdan, AR & Abu Bakar, A 2014, An evaluation of feature selection technique for dendrite cell algorithm. in 2014 International Conference on IT Convergence and Security, ICITCS 2014., 7021732, Institute of Electrical and Electronics Engineers Inc., 4th 2014 International Conference on IT Convergence and Security, ICITCS 2014, Beijing, China, 28/10/14. https://doi.org/10.1109/ICITCS.2014.7021732
Mohsin MFM, Hamdan AR, Abu Bakar A. An evaluation of feature selection technique for dendrite cell algorithm. In 2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 7021732 https://doi.org/10.1109/ICITCS.2014.7021732
Mohsin, Mohamad Farhan Mohamad ; Hamdan, Abdul Razak ; Abu Bakar, Azuraliza. / An evaluation of feature selection technique for dendrite cell algorithm. 2014 International Conference on IT Convergence and Security, ICITCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014.
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