Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

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“…The feature space

has been extremely dimensional; hence, their direct calculation leads to “dimensional disaster.” But, as

, at that point, every operation of MSVM in the feature space

is only dot products [ 28 ]. Then, kernel functions [ 29 ], i.e.,

, are effectual at handle dot product, it can be were presented as to SVM.…”

Section: The Proposed Modelmentioning

confidence: 99%

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Ragab

Eljaaly

Alhakamy

et al. 2021

Biology

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“…To achieve undersampling of majority class, Sample Subspace Optimization was proposed which uses PSO for finding global optimal solutions in the solution space. Segera et al 23 have combined PSO, PCA, and Multiclass Support Vector Machine (MCSVM) to preprocess multiclass imbalanced microarray data for analysis. Linear, Gaussian and polynomial kernels are combined and the results are optimized using PSO.…”

Section: Pso In Data Processing and Classificationmentioning

confidence: 99%

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Priya¹,

Rajappan²,

Anitha³

et al. 2021

Computational Intelligence

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Real-world datasets used for classification often face many challenges when they are imbalanced in nature which is unavoidable and need to be handled by analysts. Many researchers have proposed methods for handling imbalanced datasets and they mostly concentrated on handling binary classification with only two class labels. Only very few research works have been carried out for treating highly imbalanced datasets and fail to handle multiclass datasets. To address imbalance problem in multiclass datasets, this paper proposes a Variable Population sized Particle Swarm Optimization (VPPSO) which is a modified version of Particle Swarm Optimization (PSO) which works based on clustering. PSO usually has fixed population size and has high computational complexity. In order to reduce this, the population size is varied over generations and the particles are loaded into the population iteratively by retaining the balance nature of solutions. PSO optimizes the selection of training and testing samples from each class label in imbalanced datasets for improved classification results. From the implementation results, it is evident that using VPPSO, highly imbalanced datasets with multiclass attributes are classified more efficiently

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Metaheuristics for optimal feature selection in high-dimensional datasets

Segera

Mbuthia

Nyete

2023

Comprehensive Metaheuristics

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Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Cited by 8 publications

References 36 publications

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Metaheuristics for optimal feature selection in high-dimensional datasets

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