A Hybrid Improved Ant Colony Optimization and Random Forests Feature Selection Method for Microarray Data

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

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Random Forests Followed by Computed ABC Analysis as a Feature Selection Method for Machine Learning in Biomedical Data

Studies in Classification, Data Analysis, and Knowledge Organization - Advanced Studies in Classification and Data Science ◽

10.1007/978-981-15-3311-2_5 ◽

2020 ◽

pp. 57-69

Author(s):

Jörn Lötsch ◽

Alfred Ultsch

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forests ◽

Feature Selection Method ◽

Selection Method ◽

Biomedical Data ◽

Abc Analysis

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Pathway-Based Feature Selection Algorithm for Cancer Microarray Data

Advances in Bioinformatics ◽

10.1155/2009/532989 ◽

2009 ◽

Vol 2009 ◽

pp. 1-16 ◽

Cited By ~ 9

Author(s):

Nirmalya Bandyopadhyay ◽

Tamer Kahveci ◽

Steve Goodison ◽

Y. Sun ◽

Sanjay Ranka

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Feature Selection Method ◽

Gene Signature ◽

Selection Method ◽

Test Accuracy ◽

Clinical Markers ◽

Gene Expressions ◽

Regulatory Pathways ◽

New Feature

Classification of cancers based on gene expressions produces better accuracy when compared to that of the clinical markers. Feature selection improves the accuracy of these classification algorithms by reducing the chance of overfitting that happens due to large number of features. We develop a new feature selection method called Biological Pathway-based Feature Selection (BPFS) for microarray data. Unlike most of the existing methods, our method integrates signaling and gene regulatory pathways with gene expression data to minimize the chance of overfitting of the method and to improve the test accuracy. Thus, BPFS selects a biologically meaningful feature set that is minimally redundant. Our experiments on published breast cancer datasets demonstrate that all of the top 20 genes found by our method are associated with cancer. Furthermore, the classification accuracy of our signature is up to 18% better than that of vant Veers 70 gene signature, and it is up to 8% better accuracy than the best published feature selection method, I-RELIEF.

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MMBDE: A Two-stage Hybrid Feature Selection Method From Microarray Data

10.1109/bibm52615.2021.9669496 ◽

2021 ◽

Author(s):

Weidong Xie ◽

Yuhuan Chi ◽

Linjie Wang ◽

Kun Yu ◽

Wei Li

Keyword(s):

Feature Selection ◽

Microarray Data ◽

Feature Selection Method ◽

Selection Method ◽

Two Stage

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Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

Iranian Journal of Pediatric Hematology & Oncology ◽

10.18502/ijpho.v11i2.5838 ◽

2021 ◽

Author(s):

Razieh Sheikhpour ◽

Roohallah Fazli ◽

Sanaz Mehrabani

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Microarray Data ◽

Lymphoblastic Leukemia ◽

Feature Selection Method ◽

Selection Method ◽

Learning Methods ◽

Machine Learning Methods ◽

Acute Myeloid ◽

Sparse Feature Selection

Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expression of 7129 genes of 25 patients with acute myeloid leukemia (AML), and 47 patients with lymphoblastic leukemia (ALL) achieved by the microarray technology were used in this study. Then, the important genes were identified using a sparse feature selection method to diagnose AML and ALL tissues based on the machine learning methods such as support vector machine (SVM), Gaussian kernel density estimation based classifier (GKDEC), k-nearest neighbor (KNN), and linear discriminant classifier (LDC). Results: Diagnosis of ALL and AML was done with the accuracy of 100% using 8 genes of microarray data selected by the sparse feature selection method, GKDEC, and LDC. Moreover, the KNN classifier using 6 genes and the SVM classifier using 7 genes diagnosed AML and ALL with the accuracy of 91.18% and 94.12%, respectively. The gene with the description “Paired-box protein PAX2 (PAX2) gene, exon 11 and complete CDs” was determined as the most important gene in the diagnosis of ALL and AML. Conclusion: The experimental results of the current study showed that AML and ALL can be diagnosed with high accuracy using sparse feature selection and machine learning methods. It seems that the investigation of the expression of selected genes in this study can be helpful in the diagnosis of ALL and AML.

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