• Mi Re-Unir
    Búsqueda Avanzada
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Ver ítem 
    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
    • Ver ítem
    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
    • Ver ítem

    Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

    Autor: 
    Hans, Rahul
    ;
    Kaur, Harjot
    Fecha: 
    03/2020
    Palabra clave: 
    machine learning; feature selection; K-nearest neighbors; binary multi-verse optimization; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12695
    DOI: 
    https://doi.org/10.9781/ijimai.2019.07.004
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2734
    Open Access
    Resumen:
    Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.
    Mostrar el registro completo del ítem
    Ficheros en el ítem
    icon
    Nombre: ijimai20206_1_11_pdf_31520.pdf
    Tamaño: 915.5Kb
    Formato: application/pdf
    Ver/Abrir
    Este ítem aparece en la(s) siguiente(s) colección(es)
    • vol. 6, nº 1, march 2020

    Estadísticas de uso

    Año
    2012
    2013
    2014
    2015
    2016
    2017
    2018
    2019
    2020
    2021
    2022
    2023
    2024
    2025
    Vistas
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    73
    148
    157
    84
    Descargas
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    62
    88
    59
    43

    Ítems relacionados

    Mostrando ítems relacionados por Título, autor o materia.

    • N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents 

      Bagga, Pallavi; Hans, Rahul; Sharma, Vipul (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 12/2017)
      From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for ...
    • QoS based Web Service Selection and Multi-Criteria Decision Making Methods 

      Bagga, Pallavi; Hans, Rahul; Joshi, Aarchit (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2019)
      With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 ...
    • Nature Inspired Range Based Wireless Sensor Node Localization Algorithms 

      Arora, Sankalap; Kaur, Ranjit (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 12/2017)
      Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. ...

    Mi cuenta

    AccederRegistrar

    ¿necesitas ayuda?

    Manual de UsuarioContacto: reunir@unir.net

    Listar

    todo Re-UnirComunidades y coleccionesPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de accesoEsta colecciónPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de acceso






    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja
     
    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja