Feature Selection for Microarray Gene Expression Data Using Simulated Annealing Guided by the Multivariate Joint Entropy

González-Navarro, Félix F.; Belanche-Muñoz, Lluís A.

doi:10.13053/cys-18-2-2014-032

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…6 and 7, respectively. It was concluded that the performance of the proposed method, in terms of accuracy and efficiency, is better than other methods reported in literature (Dash, 2018;Gonzalez-Navarro and Belanche-Muñoz, 2014).…”

Section: Resultsmentioning

confidence: 61%

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Hameed¹,

Muhammad²,

Hassan³

et al. 2018

Journal of Computer Science

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In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various classifiers, thereby forming a PCC-BPSO/GA-multi classifiers approach. As such, five various classifiers are employed in the final stage of the classification. It was noticed that the PCC filter showed a remarkable improvement in the classification accuracy when it was combined with BPSO or GA. This positive impact was seen to be varied for different datasets based on the final applied classifier. The performance of various combination of the hybrid technique was compared in terms of accuracy and number of selected genes. In addition to the fact that BPSO is working faster than GA, it was noticed that BPSO has better performance than GA when it is combined with PCC feature selection.

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Section: Resultsmentioning

confidence: 61%

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Hameed¹,

Muhammad²,

Hassan³

et al. 2018

Journal of Computer Science

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“…The main advantage of SA is that it allows up-hill moves in the iteration to avoid being stuck at a local minimum. SA has been widely used as a supervised or unsupervised feature subset selection method in data mining techniques, especially for microarray gene classification in biomedical data analysis [ 41 – 43 ]. Inspired by those works, in this study, we developed a novel hybrid feature selection method by hybridizing SA with an artificial neural network (ANN).…”

Section: Methodsmentioning

confidence: 99%

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

Win

Choomchuay

Hamamoto

et al. 2018

BioMed Research International

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Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

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A comparative study of nature-inspired metaheuristic algorithms using a three-phase hybrid approach for gene selection and classification in high-dimensional cancer datasets

Hameed

Hassan

Latiff³

et al. 2021

Soft Comput

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Feature Selection for Microarray Gene Expression Data Using Simulated Annealing Guided by the Multivariate Joint Entropy

Cited by 4 publications

References 48 publications

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

A comparative study of nature-inspired metaheuristic algorithms using a three-phase hybrid approach for gene selection and classification in high-dimensional cancer datasets

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