Open AccessAn Efficient Swarm based Feature Selection Technique using Random Weight Neural Network
Author(s) -
Muhammad Manshah,
Rana Aamir Raza,
Saadia Ajmal,
Urooj Pasha,
Asghar Ali
Publication year - 2021
Publication title -
journal of nanoscope
Language(s) - English
Resource type - Journals
ISSN - 2707-711X
DOI - 10.52700/jn.v2i2.49
Subject(s) - artificial intelligence , computer science , feature selection , artificial neural network , generalization , machine learning , binary number , pattern recognition (psychology) , classifier (uml) , binary classification , feature (linguistics) , data mining , support vector machine , mathematics , mathematical analysis , linguistics , philosophy , arithmetic
Feature selection (FS) is one of the most important pre-processing tasks in machine learning (ML) and data mining, that selects optimum features by eliminating noisy and irrelevant features from the data; to improve the generalization ability of a learning model (i.e., classifier). During the classification process, data with high dimensional feature space requires different optimization techniques to obtain better predictive performance. In this paper we present a swarm intelligence based technique called binary artificial bee colony (Binary-ABC) to obtain optimum feature subset. Different binary and multiclass datasets are utilized to evaluate the performance of our proposed technique. Experimental results show that our technique provides better generalization ability with random weight neural network (RWNN), when compare with other ML classifiers.