Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

Venkataramana, Lokeswari; Jacob, Shomona Gracia; Rajavel, R.; Saisuma, Dodda; Haritha, Dommaraju; Manoja, Kunthipuram

doi:10.1007/s13258-019-00859-x

Cited by 22 publications

(8 citation statements)

References 21 publications

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“…Also, to prove the effectiveness of the proposed method, many features selection techniques are compared to the proposed features selection technique APSO. The most recent feature selection techniques used for evaluation are Hybrid Fuzzy ARTMAP and Brain Storm Optimization (FAM-BSO) [48] , Opposition-based Crow Search (OCS) algorithm [49] , Filter-Wrapper Feature Subset Selection (FWFSS) [50] , and parallelized Hybrid Feature Selection (HFS) [51] . Results are depicted in table 14 .…”

Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS 2 ) and Final Selection Stage (FS 2 ). IS 2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS 2 , which is the next stage of APSO. FS 2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty.

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Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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“…The hybridization can be divided into two strategies: one based on Fisher score and correlation coefficient features, while the other is based on mRMR and FCBF, respectively. The parallelized hybrid feature selection method is called HFS (Venkataramana et al, 2019). The HFS method includes parallelized correlation feature subset selection followed by rank-based feature selection methods.…”

Section: Othersmentioning

confidence: 99%

A Survey of Feature Selection Strategies for DNA Microarray Classification

2023

CEIS

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Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, crossvalidation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features.

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“…An important question associated with the application of non-analytical optimization methods for the determination of the most informative features is using parallel algorithms for extracting the data from the large feature sets without affecting the accuracy rate of intelligent systems. There exists a considerable body of literature on the solution of features subset selection by parallel or semi-parallel algorithms and distributed computing systems (Liu & Ditzler, 2019;Tsamardinos et al, 2019;Venkataramana et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Parallel Solution of Features Subset Selection Process for Hand-Printed Character Recognition

Ismayilov¹,

Oil²,

Mammadov³

2019

AzJHPC

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The existence of a huge amount of features for pattern recognition problems brings to the overloading of the training and exploitation steps of the recognition; also, highly correlated features affect the accuracy of the designed systems negatively. One of the most used ways for tackling this problem is the application of genetic algorithms for the solution of the binary optimization problems that appeared during the features subset selection process. In this paper was used parallel genetic algorithms for the selection of the most informative features in Azerbaijani hand-printed character recognition system by using opportunities of the distributed cluster computing. In this way after the given number of generations most appropriate features with the high recognition rate were selected from the features database.

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Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

Cited by 22 publications

References 21 publications

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

A Survey of Feature Selection Strategies for DNA Microarray Classification

Parallel Solution of Features Subset Selection Process for Hand-Printed Character Recognition

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