An Efficient Filter-Based Feature Selection Model to Identify Significant Features from High-Dimensional Microarray Data

Raj, D. M. Deepak; Mohanasundaram, R.

doi:10.1007/s13369-020-04380-2

Cited by 21 publications

(23 citation statements)

References 22 publications

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“…( BMR's) Raj, D. D., & Mohanasundaram, R. (2020) [11] Developed a new hybrid technique for gene selection known as the ensemble multipopulation adaptive genetic algorithm (EMPAGA), which can ignore irrelevant genes (EMPAGA) Shukla, A. K. (2020). [12] while accurately classifying cancer.…”

Section: Methods Author Ref Nomentioning

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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Section: Methods Author Ref Nomentioning

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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“…N is a normalization function that maintains the W (g) value to be in the interval [−1, 1]. An improvement has been suggested from Raj and Mohanasundaram, they have been proposed a new feature weighting scheme to overcome the common drawbacks of the relief family 20 …”

Section: Materials and The Methodsmentioning

confidence: 99%

Hybrid feature selection model based on relief‐based algorithms and regulizer algorithms for cancer classification

Manhrawy

Qaraad

Elkafrawy

2021

Concurrency and Computation

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Summary Cancer is a group of diseases that involve abnormal cell growth with the potential to spread to other parts of the body. Cancer microarray data usually include a small number of samples with a large number of gene expression levels as features. Gene expression or microarray is a technology that monitors the expression of the large number of genes in parallel that make it useful in cancer classification, high dimensionality in cancer microarray data results in the overfitting problem. This article proposes novel hybrid feature selection model called the RBARegulizer model, which is based on two types of feature selection techniques, two RBAs algorithms (ReliefF, MultiSURF) for feature‐ranking filters to the most important one's genes, and three regulizer algorithms (Lasso, Elastic Net, Elastic Net CV) to reduce the feature subset, remove the noisy and irrelevant feature to improve the performance and accuracy of cancer (microarray) data classification. For evaluating the model, the different three classifiers SVM, MLP, and random forest with four high‐dimensional microarray data for different cancer types were applied. The experimental type shows that our model overcomes the overfitting problem of cancer microarray data. Moreover, the results show that RBARegulizer model is perfect in improving the accuracy of cancer microarray data classification.

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“…The results of classification performance successfully selected 12 out of 23 original features as the most significant features with 86.78% of accuracy. Raj and Mohanasundaram [ 44 ] suggests an improved feature weighting algorithm named Boundary Margin Relief (BMR) based on IG, RF, Multi-SURF, and I-Relief to identify significant features from high dimensional microarray datasets with the highest redundancy. This method successfully produces 345 significant features out of 7128 dataset features and improves SVM accuracy by 92.30%.…”

Section: Literature Studymentioning

confidence: 99%

“…The improvement in each classifiers accuracy using ensemble RF, IG, GR, and CS in Lee et al [ 29 ], Santos et al [ 49 ], and Dongare et al [ 16 ] indicate that ensemble filters are capable in selecting significant amount of features by considering dependency between features. Furthermore, the experiment conducted by Raj and Mohanasundaram [ 44 ] clearly shows that the accuracy improved significantly to 92.30% compared to the accuracy with full dataset features. This shows that multi-filters algorithm feature selection is capable to improve the classification accuracy while producing an optimal number of selected features.…”

Section: Literature Studymentioning

confidence: 99%

“…The typical feature selection techniques are known as filter, wrapper, and embedded algorithms. Selection of features via filter algorithms is commonly performed by evaluating features based on dependency, entropy value, weightage score, or distance measure using a simple ranking principle, where the significance of each feature is ranked according to their intrinsic score values [ 44 ]. Filter algorithms do not require any involvement of classifier algorithms, which made them independent and highly effective for handling data with high dimensionality.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

Hamid

Sallehuddin

Yunos

et al. 2023

Neural Comput & Applic

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Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.

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An Efficient Filter-Based Feature Selection Model to Identify Significant Features from High-Dimensional Microarray Data

Cited by 21 publications

References 22 publications

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

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

Hybrid feature selection model based on relief‐based algorithms and regulizer algorithms for cancer classification

Ensemble filters with harmonize PSO–SVM algorithm for optimal hearing disorder prediction

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