Improved Genetic Particle Swarm Optimization and Feature Subset Selection for Extreme Learning Machine

2018 ◽  
Vol 06 (01) ◽  
pp. 48-54
Author(s):  
A.P. Kale (IEEE ◽  
IEICE Student Member) ◽  
S.P. Sonavane
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Selection For ◽  
Learning Machine

Particle Swarm Optimization Algorithm and its Hybrid Variants for Feature Subset Selection

2013 ◽  
pp. 449-466
Author(s):  
Basabi Chakraborty
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Data Sets ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Machine Learning Applications ◽  
Mathematical Techniques ◽  
Optimal Feature Subset

Selecting an optimum subset of features from a large set of features is an important pre- processing step for pattern classification, data mining, or machine learning applications. Feature subset selection basically comprises of defining a criterion function for evaluation of the feature subset and developing a search strategy to find the best feature subset from a large number of feature subsets. Lots of mathematical and statistical techniques have been proposed so far. Recently biologically inspired computing is gaining popularity for solving real world problems for their more flexibility compared to traditional statistical or mathematical techniques. In this chapter, the role of Particle Swarm Optimization (PSO), one of the recently developed bio-inspired evolutionary computational (EC) approaches in designing algorithms for producing optimal feature subset from a large feature set, is examined. A state of the art review on Particle Swarm Optimization algorithms and its hybrids with other soft computing techniques for feature subset selection are presented followed by author’s proposals of PSO based algorithms. Simple simulation experiments with benchmark data sets and their results are shown to evaluate their respective effectiveness and comparative performance in selecting best feature subset from a set of features.

Feature Subset Selection Using Binary Quantum Particle Swarm Optimization for Spam Detection System

2014 ◽  
Vol 20 (1) ◽  
pp. 188-192 ◽  
Author(s):  
Amir Rajabi Behjat ◽  
Aida Mustapha ◽  
Hossein Nezamabadi-Pour ◽  
Md. Nasir Sulaiman ◽  
Norwati Mustapha
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Detection System ◽  
Particle Swarm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Spam Detection ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

scholarly journals Boosted Relief Feature Subset Selection and Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization

2020 ◽  
Vol 8 (5) ◽  
pp. 2605-2613
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Swarm Optimization ◽  
Software Defect ◽  
Cross Project

The exponential growth in the field of information technology, need for quality-based software development is highly demanded. The important factor to be focused during the software development is software defect detection in earlier stages. Failure to detect hidden faults will affect the effectiveness and quality of the software usage and its maintenance. In traditional software defect prediction models, projects with same metrics are involved in prediction process. In recent years, active topic is dealing with Cross Project Defect Prediction (CPDP) to predict defects on software project from other software projects dataset. Still, traditional cross project defect prediction approaches also require common metrics among the dataset of two projects for constructing the defect prediction techniques. Suppose if cross project dataset with different metrics has to be used for defect prediction then these methods become infeasible. To overcome the issues in software defect prediction using Heterogeneous cross projects dataset, this paper introduced a Boosted Relief Feature Subset Selection (BRFSS) to handle the two different projects with Heterogeneous feature sets. BRFSS employs the mapping approach to embed the data from two different domains into a comparable feature space with a lower dimension. Based on the similarity measure the difference among the mapped domains of dataset are used for prediction process. This work used five different software groups with six different datasets to perform heterogeneous cross project defect prediction using firefly particle swarm optimization. To produce optimal defect prediction in the Heterogeneous environment, the knowledge of particle swarm optimization by inducing firefly algorithm. The simulation result is compared with other standard models, the outcome of the result proved the efficiency of the prediction process while using firefly enabled particle swarm optimization.

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