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svgpm

This is the source file for SVGPM

https://github.com/spozi/svgpm

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

This is the source file for SVGPM

Basic Info
  • Host: GitHub
  • Owner: spozi
  • Language: C++
  • Default Branch: main
  • Size: 130 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme Citation

README.md

This is C++ based implementation for SVGPM.

In order to build this program, please install shark machine learning library version 3 into your system https://github.com/Shark-ML/Shark/tree/3.1.1.

This program has been sucessfully built and run on WSL2 Ubuntu 20.04 Based Distro.

In order to run the program (once it has been built):
go to /build/app
run svgpm on command line/console:
svgpm -gen 100 -p 100 -ds wine_quality.csv.

The program will run for 100 generation (-gen 100), with population 100 (-p 100), on dataset winequality.csv (-ds winequality.csv) using 5-fold cross-validation.

This work has been used in the following papers:

  1. Pozi, M. S. M., Azhar, N. A., Raziff, A. R. A., & Ajrina, L. H. (2021). SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem. Progress in Artificial Intelligence, 1-13.
  2. Pozi, M. S. M., Sulaiman, M. N., Mustapha, N., & Perumal, T. (2016). Improving anomalous rare attack detection rate for intrusion detection system using support vector machine and genetic programming. Neural Processing Letters, 44(2), 279-290.
  3. Mohd Pozi, M. S., Sulaiman, M. N., Mustapha, N., & Perumal, T. (2015). A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification. Remote Sensing Letters, 6(7), 568-577.

Owner

  • Name: Muhammad Syafiq Mohd Pozi
  • Login: spozi
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Pozi"
  given-names: "Muhammad Syafiq Mohd"
  orcid: "https://orcid.org/0000-0001-9379-7351"
- family-names: "Azhar"
  given-names: "Nur Athirah"
- family-names: "Abdul Raziff"
  given-names: "Abdul Rafiez"
- family-names: "Ajrina"
  given-names: "Lina Hazmi"
title: "SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem"
preferred-citation:
  type: article
  authors:
  - family-names: " Mohd Pozi"
    given-names: "Muhammad Syafiq"
    orcid: "https://orcid.org/0000-0001-9379-7351"
  - family-names: "Azhar"
    given-names: "Nur Athirah"
  - family-names: "Abdul Raziff"
    given-names: "Abdul Rafiez"
  - family-names: "Ajrina"
    given-names: "Lina Hazmi"
  doi: "10.1007/s13748-021-00260-4"
  journal: "Progress in Artificial Intelligence"
  month: 8
  start: 1 # First page number
  end: 13 # Last page number
  title: "SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem"
  year: 2021

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