A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Qaraad, Mohammed; Amjad, Souad; Manhrawy, Ibrahim I. M.; Fathi, Hanaa; Hassan, Bayoumi Ali; Kafrawy, Passent El

doi:10.1109/access.2021.3065341

Cited by 26 publications

(8 citation statements)

References 35 publications

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“…2021 ) 0.46 0.51 23.4 0.01 74.2 0.49 0.54 13.82 0.01 45.15 0.8 0.82 2.8 0.01 8.29 DummyRegressor (Kwan-to 2001 ) −0.11 −0.01 33.75 0.01 73.69 −0.1 0 20.3 0.01 44.12 −0.11 −0.01 6.61 0.01 7.95 ElasticNet (Hans 2011 ) 0.45 0.51 23.62 0.01 74.89 0.45 0.5 14.4 0.01 45.12 0.62 0.66 3.84 0.01 8.34 ElasticNetCV (Qaraad et al. 2021 ) 0.49 0.54 22.81 0.07 74.53 0.48 0.53 13.98 0.07 45.04 0.77 0.79 3.04 0.08 8.29 …”

Section: Discussionmentioning

confidence: 99%

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

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Section: Discussionmentioning

confidence: 99%

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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“…SVM is a classification algorithm based on mathematical learning theory [21], [22]. SVM (Support Vector Machine) has long been praised for its superior classification efficiency and intrinsic feature selection ability.…”

Section: B Support Vector Machinesmentioning

confidence: 99%

An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

Elkafrawy

Fathi²,

Qaraad

et al. 2021

IEEE Access

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Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples, the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe). The proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.

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“…The biggest concern of the FS process is to discard the irrelevant, redundant, and noisy features from the whole set of features to derive a subset of representative features. This process is used in many areas of science such as data classification [4], image processing [5], text categorization [6], data clustering [7], and signal processing [8]. The primary objective of the FS process is to find OFS from highly discriminated features that result in high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Al-Shourbaji

Kachare²,

Alshathri

et al. 2022

Mathematics

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Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented to select the most Optimal Features Subset (OFS) in a given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods in a parallel mechanism to determine OFS. This mechanism decreases the chance of the two methods to stuck in local optima and it boosts the capability of both of them to balance exploration and explication. Numerous experiments are performed on ten datasets taken from the UCI repository and two real-world engineering problems to evaluate RSA-SO. The obtained results from the RSA-SO are also compared with seven popular Meta-Heuristic (MH) methods for FS to prove its superiority. The results show that the developed RSA-SO approach has a comparative performance to the tested MH methods and it can provide practical and accurate solutions for engineering optimization problems.

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A Hybrid Feature Selection Optimization Model for High Dimension Data Classification

Cited by 26 publications

References 35 publications

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

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