A HYBRID OF GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE FOR FEATURES SELECTION AND CLASSIFICATION OF GENE EXPRESSION MICROARRAY

2005 ◽  
Vol 05 (01) ◽  
pp. 91-107 ◽  
Author(s):  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
ROSLI MD ILLIAS
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Small Subset ◽  
Features Selection ◽  
Gene Expression Microarray ◽  
Expression Microarray

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

scholarly journals An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

2021 ◽  
Vol 18 (17) ◽  
Author(s):  
Micheal Olaolu AROWOLO ◽  
Marion Olubunmi ADEBIYI ◽  
Chiebuka Timothy NNODIM ◽  
Sulaiman Olaniyi ABDULSALAM ◽  
Ayodele Ariyo ADEBIYI
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Malaria Vector ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Rna Seq ◽  
Gene Expressions

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT

تطبيق تصنيف بيانات اللوكيميا باستخدام خوارزمية AGA-SVM Applying of Classification Leukemia's Dataset using AGA-SVM Algorithm

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
عمر صابر قاسم ◽  
محمد علي محمد
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Data Classification ◽  
Feature Space ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Features Selection ◽  
Svm Algorithm

تعد مسألة اختيار الميزات (Features selection) الضرورية في عملية تصنيف البيانات (Data Classification) من المسائل ذات الأهمية الكبيرة في تحديد كفاءة التقنية المستخدمة للتصنيف خصوصا عندما يكون حجم هذه البيانات كبيرا جدا مثل بيانات اللوكيميا (leukemia) المعتمدة على الجينات. اذ تم استخدام خوارزمية مقترحة(AGA_SVM) مهجنة بين الخوارزمية الجينية المعدلة (Adaptive Genetic Algorithm) مع تقنية الة المتجه الداعم (Support Vector Machine)، اذ تقوم الخوارزمية الجينية المعدلة بتحويل البيانات من فضاء الأنماط العالي البعد (High-D Patterns Space) إلى فضاء الخواص الواطئ (Low-D Feature Space) لأجل تحديد الميزات الضرورية واللازمة لعملية التصنيف والتي تتم من خلال تقنية الة المتجه الداعم. وتبين من خلال التطبيق على بيانات اللوكيميا ان نسبة التصنيف كانت (100%) لحالات التدريب والاختبار بالنسبة للطريقة المقترحة (AGA_SVM) مقارنة مع الطريقة الاعتيادية التي أخطأت في عدة حالات تصنيف، مما يدل على كفاءة الطريقة المقترحة مقارنة مع الطريقة الاعتيادية.

A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading

2013 ◽  
Vol 14 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Christian L Dunis ◽  
Spiros D Likothanassis ◽  
Andreas S Karathanasopoulos ◽  
Georgios S Sermpinis ◽  
Konstantinos A Theofilatos
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Hybrid Genetic Algorithm ◽  
Support Vector

scholarly journals Construction and Practice of the Optimal Smooth Semi-Supervised Support Vector Machine

2015 ◽  
Vol 3 (5) ◽  
pp. 398-410 ◽  
Author(s):  
Xiaodan Zhang ◽  
Ang Li ◽  
Pan Ran
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Spline Function ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Unconstrained Optimization Problem

AbstractThe standard semi-supervised support vector machine (S3VM) is an unconstrained optimization problem of non-convex and non-smooth, so many smooth methods are applied for smoothing S3VM. In this paper, a new smooth semi-supervised support vector machine (SS3VM) model , which is based on the biquadratic spline function, is proposed. And, a hybrid Genetic Algorithm (GA)/ SS3VM approach is presented to optimize the parameters of the model. The numerical experiments are performed to test the efficiency of the model. Experimental results show that generally our optimal SS3VM model outperforms other optimal SS3VM models mentioned in this paper.

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