Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification

Namous, Feras; Faris, Hossam; Heidari, Ali Asghar; Khalafat, Monther; Alkhawaldeh, Rami S.; Ghatasheh, Nazeeh

doi:10.1007/978-981-32-9990-0_11

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…In our experiments, we consider the classification error rate more important than feature subset length and set

α \in false(0,1 false)

β = 1 - α, α

= 0.95 and β = 0.05. This is because the performance of the data subset can be improved by the classification error rate reflecting the training results of the training set 16,50,124,125 . The merit of each feature subset is evaluated by this fitness function, and the smaller the fitness, the better the feature subset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Heidari

Zhang

et al. 2021

Int J of Intelligent Sys

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Grey wolf optimization (GWO) is a widely used metaheuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima,

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“…In our experiments, we consider the classification error rate more important than feature subset length and set

α \in false(0,1 false)

β = 1 - α, α

Section: Experiments and Resultsmentioning

confidence: 99%

“…This is because the performance of the data subset can be improved by the classification error rate reflecting the training results of the training set. 16,50,124,125 The merit of each feature subset is evaluated by this fitness function, and the smaller the fitness, the better the feature subset.…”

Section: Fitness Functionmentioning

confidence: 99%

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Heidari

Zhang

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another work, Namous et al [50] transformed the usual fitness function based on classification efficiency into two more efficient fitness functions, namely, the area under the ROC curve and the g-mean. Two common metaheuristic techniques were tested with the three fitness functions for classifying six unbalanced datasets in order to evaluate the efficacy of the methodology.…”

Section: Related Workmentioning

confidence: 99%

Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems

Alweshah,

Almiani,

Alkhalaileh

et al. 2023

IEEE Access

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An imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety of classification difficulties. On the other hand, metaheuristic optimization approaches offer an excellent means by which to deal with this problem. Therefore, this work combines two metaheuristic algorithms-the Ali Baba and the Forty Thieves (AFT) algorithm and the Water Strider Algorithm (WSA)-in order to alter the weights of a PNN classifier for imbalanced datasets. This article introduces a self-contained multiple-search approach for parallel metaheuristics that may be used in a variety of situations. Most implementations begin many search processes, all of which utilize the same search algorithm, with a set of starting parameters that are all generated separately. Most implementations pick a processor to collect data and verify the data for compliance with some stopping criteria, with the latter being the default. In the proposed AFT-WSA parallel method, the two algorithms begin simultaneously, and the fitness value is communicated in each iteration to find the best classification accuracy in the smallest number of iterations, thereby allowing the weight of the PNN classifier to be adjusted. In this study, ten imbalanced public datasets were used to test the performance of the proposed approach in terms of classification accuracy, standard deviation, and F-measure.

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“…• Evolutionary Search is an iterative process in the form of generation to refine the population with fittest solutions using three main steps as in (De La Iglesia 2003;Namous et al 2020). These steps include:…”

Section: Feature Selection Phasementioning

confidence: 99%

The Impact of Software Team Project Measurements on Students' Performance in Software Engineering Education

2020

JEP

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It is essential to the software engineering instructors to monitor the students' performance in their course projects. Detecting key measures of software engineering project helps to get a better assessment for students' performance, resolve difficulties of low expectation-team's, and consequently improves the overall learning outcomes. Several studies attempted to present the important measures of software project but they only captured the early phases of the whole project time period. This paper introduces a hybrid approach of classification and feature selection techniques, which aims to comprehensively cover all phases of software development through investigating all product and process measures of software project. Experiments were conducted using five classifiers and two feature selection techniques. The results show the significant process and product measures for the software engineering team projects, which primarily improves the students' performance assessment. The performance prediction of our proposed assessment model outperforms prediction of the previous models.

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Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification

Cited by 8 publications

References 11 publications

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems

The Impact of Software Team Project Measurements on Students' Performance in Software Engineering Education

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